Patrick Arthur Donaldson
On Evolution-Assisted Design (EAD) by Computers
Tutored by Daniel Pinkas HEAD Geneva, 2016 - 2017
To my family and friends
Design
Computing
Biology
EAD by computers
Abstract The theory of evolution was possibly the most crucial discovery in the history of mankind. Its changes in the field of biology were ground-breaking. With further research, it has also impacted the field of computing and given rise to some of the most powerful software and hardware used today. However, the impact in the field of design seems not to have taken place. Why is this so ? Why would such a powerful algorithm as the one for evolution be ignored in the process of design ? I childishly thought I would find a revolutionary method that would change the process of design forever. I thought media designs could become autonomous self-evolving digital organisms, exceeding their expected lifetime, forever adapting to the needs of the designer. With a short historical overview and a piecemeal decomposition of the evolutionary dynamics based on books such as On the Origin of Species by Darwin and The Selfish Gene by Dawkins, this thesis attempts to show the relevance and potential usefulness of Evolution-Assisted Design by computers. Through case analyses and the creation of genetic algorithms, one will see why the creation of autonomously evolving designs cannot happen in a desired fashion without the careful interventions of a watchful guardian. Nevertheless, certain solutions may permit designers to consider the use of such algorithms for their own adaptable projects ; forcing the designer to adopt a radically different approach.
Preface 017
Hello world
From sea, to land, to digital 021 026 030 034 058
Ex nihilo The fathers of digital life Status artis Status artis — Portfolio Conclusion
Complexity from simplicity 063 064 071 077 090
Introduction Evolution extended The process of design In vivo versus in silico Conclusion
Writing the genetic book of the dead 095 097 101
Introduction Divide ut regnes Conclusion
Future horizons 105 106 108
Introduction Not without man Forgive me father for I have sinned ?
Annexes 113 118 121 130 133
Bibliography Iconography Shaping Mount Improbable Glossary Acknowledgements
Preface
1.  Processing is a software based on the programming language Java by Ben Fry and Casey Reas. Since 2001, it has taught the basics of programming and generative design to many.
Hello world Computers have always been a major part of my life. Growing up, I was surrounded by technology and found myself manipulating computer code quite young. Through the years, I developed a keen interest in automation —looking for practicalness— which allowed me to approach my projects from an algorithmic angle. I would always ask myself the questions : How can I script this ? What are the variables here ? Can the computer do this for me ? Having studied science, I never thought I would engage in the creative path. However, when the time came to make a choice, I chose visual communication. I soon figured out that I could combine my scientific background with my creativity. In doing so, I discovered the field of generative design with tools such as Processing1 (Fry & Reas, 2001), and found myself creating my own software to generate graphical designs and tools. Continuously striving to better my practice, I wish to dive deeper into the intersection between science and design, and uncover what the next step may be for media designers.
Hello world | 017
From sea, to land, to digital
Ex nihilo There are currently 8.7 million different species crawling, swimming, walking, and flying on the surface of planet earth —give or take 1.3 million— but it hasn’t always been so. A very long time ago, the intricate reactions between particles, atoms, and molecules gave rise to the first ever organisms in the primordial soup. With the appearance of deoxyribonucleic acid (DNA), certain organisms gradually mutated and passed down their genetic information through generations, forever adapting to the highly competitive environment they inhabited. These changes have given rise to the existence of marvellous and complex structures, such as wings, eyes, biosonar organs, and plant burrs. This is Charles Darwin’s2 theory of evolution through natural selection (1859) : “If during the long course of ages and under varying conditions of life, organic beings vary at all in the several parts of their organisation, and I think this cannot be disputed ; if there
2. Charles Darwin first presented his theory in 1859 after an extensive search and analysis of botanical and zoological data. He was helped by Alfred Russel Wallace and many others who inspired his research and also predicted it. It finally gave a sound explanation to the existence of so many species and their origin.
be, owing to the high geometrical powers of increase of each species, at some age, season, or year, a severe struggle for life, and this certainly cannot be disputed ; then, considering the infinite complexity of the relations of all organic beings to each other and to their conditions of existence, causing an infinite diversity in structure, constitution, and habits, to be advantageous to them, I think it would be a most extraordinary fact if no variation ever had occurred useful to each being’s own welfare, in the same way as so many variations have occurred useful to man. But if variations useful to any organic being do occur, assuredly individuals thus characterised will have the best chance of being preserved in the struggle for life ; and from the strong principle of inheritance they will tend to produce offspring similarly characterised. This principle of preservation, I have called, for the sake of brevity, Natural Selection.” (p. 126) Ex nihilo | 021
Cactospiza pallida Geospiza magnirostris
Cactospiza heliobates
Geospiza fortis
Geospiza scandens
Camarhynchus psittacula
Geospiza fuliginosa Geospiza conirostris
Camarhynchus pauper
Granivorous Camarhynchus parvulus
Insectivorous
Cactus-feeding
Certhidea olivacea
Pinaroloxias inornata
Platyspiza crassirostris Geospiza difficilis
Vegetarian
Tree Finches
Ground Finches
Ancestral Finch species
Warbler-like
Thanks to advances in science, it is now known that variation occurs at the genetic level and that these changes will be apparent in traits at the phenotypic level. If these slight variations permit a better performance3 in the struggle for life, they are guaranteed to be selected and inherit-
3. Performance, not only in means of predation, but also fecundity, symbiosis, endosymbiosis...
ed by the next generation through reproduction (sexual or asexual). Darwin’s theory provoked great turmoil between the religious and scientific communities as it directly challenged Creationism. Thanks to the support of many biologists, it has been accepted by the vast majority of the scientific community that this diversity of species does not require supernatural causation. It may seem unfathomable to many that things that look designed, such as flora and fauna, could have come into existence without the help of a divine creator or intelligent designer. In The Blind Watchmaker (1986), Richard Dawkins4 differentiated the process of a watchmaker from natural selection and showed that, even though organisms in nature compel belief in an intelligent designer, they are the result of a mindless, purposeless process achieved by nature ; analogous to a blind watchmaker, hence the term “blind designer”.
4. Richard Dawkins, an English evolutionary biologist, is the author of The Selfish Gene which popularised the gene-centric view of evolution. Through various other publications, he criticises the idea of Creationism and religious faith.
Another common misconception linked to the fact that certain species may look like the conscious designers of their own phenotypic productions. Most spiders, for example, have evolved an exceptional skill in catching prey with sophisticated webbing. Although spider webs may look designed, they are not. The existence of these webs is the result of countless trials and errors in fly-catching. Cobwebs, like the spiders themselves, are what Dawkins (1991) calls “designoids”.5 To illustrate this, Fuchs and Krink (1997) developed NetSpinner, a computer program that recreates spider web patterns. The ones who perform best at
5. “Designoid” objects have an internal and external complexity that make one believe they have been exquisitely created for a specific purpose.
capturing flies are then selected, slightly mutated and tested again. It is then quite clear how evolution through the accumulation of random successful variations can produce such complexity and efficacy.
Opposite A diagram of the evolution of finches in the Galàpagos islands that was an important milestone in the development of Darwin’s theory.
Ex nihilo | 023
Above  A few screen shots of the software NetSpinner evolving spider webs.
Nevertheless, there are exceptions to designoids. The evolution of certain life forms has granted them a heightened understanding of their environment and how they might use it to their advantage. Dolphins, for example, will use sea sponges to protect their sensitive snouts when foraging for food on the sea floor (Garber, 2014). These dolphins, called “spongers”, have developed the use of tools.6 Crows are also known to use tools in their daily lives (Gray, 2010). One particularly advanced 7
species in the development of tools is homo sapiens. Indeed, from the moment sticks and stones were picked up and used, creating tools or devising ideas for new tools has slowly become an integral component of the human mind, pushing back the limitations of the human body.8 Personal computers are the latest in a long line of tools. Their arrival in the 1970s brought on an abundance of functionality that reshaped the productivity and thinking process of designers, chang-
6. Tools, in opposition to “designoids”, are truly “designed” ; constructed with intent to serve a particular purpose in the foreseeable future. 7. Crows use elements around them such as branches to create hooks which aid them in their hunt for grubs. 8. The hand axe is among the first prehistoric tools to be called as such. Its apparition and manipulation covers the whole paleolithic era (which spans from 2.6 mya to 10’000 years ago) before other tools appeared. One of the most important recent developments, printing, profoundly changed the way information is managed.
ing in more than a few respects the fundamentals of the design process. In opposition to the knapping of stone, written code is easily restructured and rewritten. Through this new medium, designers, engineers and artists have been able to solve problems, extract data, modularise processes, and express their inventiveness differently. In this paper, I wish to discuss the creation of “organisms” to generate modern media designs. Exploring the field of biology, genetics, and their emulation in the digital realm, I will put forward the relevance, benefits, and difficulties of an evolutionary approach, in an attempt to answer the following question : why, if evolution is so powerful, is it not inherent in the designer’s tools, or part of the designs themselves ? A tool to evolve media designs would, in my opinion, greatly improve the work flow of creators ; and encompassing evolution directly in productions can, I believe, help create autonomous self-evolving designs which would vary over time. These would forever adapt to the demands of the designer. The potentially resulting prolongation of life-expectancy of designs would make it necessary to rethink the fundamentals of design and the role of designers. Ex nihilo | 025
The fathers of digital life Drawing inspiration from designoids in nature is an important innovation factor in the development of technologies. It has now extended to digital art and design ; from the concoctions of Leonardo da Vinci to the simulations of flocking behaviour in generative art today. Of course, since nature has had billions of years to improve these functionalities, it would be foolish not to draw inspiration from them. But these designs are merely copying the effects of the cause. Instead, one could perhaps emulate the cause, namely evolution itself, to produce a particular solution. In the very early days of computing, certain researchers were already pondering the use of computers to mimic the process of evolution. In the 1950s, an Italian mathematician by the name of Nils Aall Barricelli imagined the computer as a universe where numbers were organisms (Hackett, 2014). Using the IAS machine, Barricelli ran the first models of evolution using punch cards. Randomly selecting numbers thanks to a deck of playing cards, he simulated the first artificial life-like system and essentially the first artificial intelligence (AI). Through iterative calculations of relationships with preceding rows of numbers, the universes would visually rearrange in the search of optima. Alex Fraser, an Australian quantitative geneticist, pioneered the simulation of genetic systems such as bacteria on computers. His work on the development of computational models of evolutionary systems is quoted to this day. However, it is John Henry Holland, an American professor from the university of Michigan, who in 1975 initiated the theoretical field of genetic algorithms (GAs) and explored their possible applications. He is now widely recognized as the father of evolutionary algorithms (EAs) in the field of artificial life (A-life).
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Opposite top  The IAS machine was the first electronic computer built at the Institute for Advanced Study, in Princeton, New Jersey. It was designed and built between 1945 and 1951 under the supervision of the famous polymath John von Neumann. Opposite bottom  Barricelli was possibly the first to export computed 1s and 0s into pictorial images. Once converted into images, one can easily notice the chaos of mutation in the centre, and the adapted system on the right.
GAs, a subset of EAs, mimic natural selection by describing a design as if it were a genome constructed from digital genes. Each gene describes a parameter of the phenotypic expressions (e.g. shape, colour...). By randomly changing some genes and selecting them, the algorithm improves the design. The best results (whatever criteria for “best” are chosen) are then bred together and further iterated. EAs have a heuristic or stochastic character as opposed to a deterministic one ; that is, they involve the use of incomplete information, and through the generation of random variables, explore a number of solutions that would be too numerous to explore otherwise. Once a better solution is found, the algorithm ensures its survival. EAs comprise many partially overlapping approaches such as genetic programming, evolution strategy, differential evolution, neural networks, ant colony optimisation, particle swarm optimisation and more. Natural selection, and by extension EAs, are oftentimes misunderstood to be an unstoppable progression towards perfection, but as Darwin (1859) himself explained, the progression is only relative : “It may be said that natural selection is daily and hourly scrutinising, throughout the world, every variation, even the slightest ; rejecting that which is bad, preserving and adding up all that is good ; silently and insensibly working, whenever and wherever opportunity offers, at the improvement of each organic being in relation to its organic and inorganic conditions of life.” (p. 133) It is clear that evolution is merely a system to pick a better adaptation to a given environment, but this adaptation may not be the best —absolutely speaking—, only one that has proven to work in the current conditions.
Opposite Alexander Galloway, an associate professor in the department of media, culture, and communica-
tion at New York University, recreated Barricelli’s work in Processing to see the groups of evolving organisms in colour.
The fathers of digital life | 029
Status artis Today, it is computer scientists such as Peter J. Bentley who are exploring the possible applications and boundaries of A-life and biology-inspired computing. A field which he terms “digital biology” : “We use such digital biology to evolve solutions to problems, such as methods for detecting fraud. We explore how immune systems can be created within computer networks and used to attack hackers. We discover how to use colonies of computational ants to search for better solutions to scheduling problems. We examine how architectural designs can be grown from a set of digital genes into adult form. We find out how to use digital neural networks to detect the difference between benign and malignant cancer cells. We learn how to develop colonies of digital cells that have the behaviour of fire. By using the natural processes responsible for life within computer software, we are overturning all preconceptions of what computers can and cannot do.” (Bentley, 2001, p. 13) The first commercial product harnessing the power of evolution was Evolver (1990), an optimisation tool for spreadsheets. Using a GA, the software is used to improve timetables in hotels, flight times in airports, inventory management based on client reservations, fuel usage and frequency of employment. Although released in 1990, it is still in use today. This hints to the power and adaptivity of this particular EA. Evolution has also made headway in hardware since the mid-1990s : Dr. Adrian Thompson worked on a chip (a field-programmable gate array) whose ones and zeros are rearranged following the processes of natural selection to optimise demanded functionalities (Bellows,
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Opposite A screen-shot of Evolver optimising the production of bread for an industrial bakery.
2013). It was quite baffling for Thompson to discover that, as well as providing the best way to execute the functions (e.g. using less logic gates), the chip optimized its use of the provided microchip environment and the subtle changes in the electromagnetic field. Interestingly, the chip evolved in such a way that suppressing logic gates, suspected not to provide any useful function, would cause the whole system to crash. Why this is so has still not been explained. Such experiments are slowly opening the doors to “evolvable hardware”. Evolvable hardware, through a digital biology approach, creates advanced electronics without manual interventions. This is most useful when conventional design processes cannot provide sufficient information for all desired behaviours or cover the whole spectrum of possible situations. Self-replicating machines or spacecraft such as von Neumann probes9 could then survive the rigours of deep-space exploration by self-adapting when damaged by radiation (Tzezana, 2016).
9. Named after John von Neumann, whose theory of automata revolved around the concept of self-replicating machines.
In 2006, NASA produced an optimal antenna shape that emits better radiation patterns than standard antennas. The antenna was designed with the help of a GA and was the first artificially-evolved
Opposite The resulting antenna obtained after evolution by NASA.
object to enter outer space. The team at NASA, starting in the mid1990s, used evolution for optimisation. GAs were also used to improve the shape of aircrafts10 and propagation of acoustics.11 Today, using an evolutionary approach in synthetic biology, semiconductors can be evolved to work in any environment (Bawazer, 2013). Undoubtedly, digital biology is providing solutions for the advances of science and technology. However, could this process of evolution by natural selection be used as effectively in the field of generative design to produce evolving media designs ?
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10. In 1998, Obayashi et al. optimised the design of supersonic wings using an EA. Thus minimising aerodynamic drag at supersonic and subsonic cruising speeds. This approach also reduced the bending forces felt by the wings. 11. Sato et al. (2004) maximised the sound qualities of a concert hall by using a GA. By inputting various acoustic properties to improve, the shape of the hall evolved from a simple box to a leaf-shaped room.
Status artis — Portfolio Although quite scarce, there are certain media design projects that have drawn inspiration from evolution in biology. Indeed, due to the demanded skills in programming, these evolutionary projects are often oriented towards research. This following part covers a selection of the most interesting and challenging implementations of evolutionary dynamics and those I judge most relevant for the my research questions.
Opposite  A list of terms used to categorise the following projects.
Types of GAs
Cellular automata
Interactive selection
Cellular automata are grid-based simulations initialised with a precise set of rules. Through the generations, a wide range of patterns can emerge.
Interactive selection is highly dependent on the user, it demands that a human manually inputs the fitness of organisms, or, selects the preferred organism for the following generation altogether.
Divided interactive selection Divided interactive selection takes advantage of many participants to define the fitness of an organism.
Ecosystem simulation Ecosystem simulations render small universes with an environment, global constants, and artificial organisms.
Seeded evolution Seeded evolution is a type of GA where the initialisation of a population is done with manually constructed organisms that are then evolved in the machine.
Game of Life
1970
By John Conway Type : Cellular automata Game of Life is a cellular automaton (Gardner, 2010). Cellular automata, are grid-based simulations that evolve patterns over time. Each cell is in one of a finite set of states (e.g. “living” or “dead”). According to a set of rules, the cells will change their state based on that of neighbouring ones. Through generations, the simulations evolve toward one of four states : homogeneity, oscillating structures, chaotic structures, and extremely complex structures. This production of extreme complexity through simple mathematical rules convinced Conway that intelligence must have arisen through such a process. The cellular automaton has many applications (e.g. cryptography) and proved to be capable of simulating real-world biological systems. Although the system does not comprise all three steps of evolution, it is part of the first explorations of A-life.
Opposite A screen-shot of a cellular automaton after a few generations.
Biomorphs
1986
By Richard Dawkins Type : Interactive selection Integral to his books, The Blind Watchmaker and Climbing Mount Improbable, Biomorphs are digital creatures which evolve by the hand of the user. In the original program, one selects the next evolutionary step of the organism out of eight random possibilities with the mouse of the computer. When the selection is made, a close to perfect genotypic inheritance generates the next possible organisms with slight phenotypic differences. The user may select again and so on, grasping in a few clicks the power of Darwinian selection.
Opposite  A collection of the very first Biomorphs generated in 1986.
GenJam
1993
By Al Biles Type : Seeded evolution Biles created a genetic algorithm that improvises jazz that he can then play the trumpet to. Short for genetic jammer, GenJam is an interactive software which listens to the musician and then responds with melodic improvisations while providing a general rhythmic structure through other instruments (e.g. percussion and bass). Just as in real jam sessions, Biles provides the computer with a partition which contains the base chord progression. The musician starts the conversation by playing a tune over the background. This tune is then recorded into “chromosomes” with the help of a microphone which tracks the pitch and sends a MIDI event to the computer. The chromosomes will evolve and play back the new melody at the end of a certain number of bars. Interestingly, GenJam is unable to play any wrong notes, unlike Biles.
Opposite top Al Biles presenting GenJam at a TED conference. Opposite bottom Al Biles playing with his tool GenJam.
Evolved Virtual Creatures By Karl Sims Type : Ecosystem simulation Sims applied Darwinian evolution to the virtual world and created various block creatures. Hundreds of organisms are simulated to produce the most efficient creatures in swimming, walking, following, jumping or other. The successful behavioural patterns slowly emerge from the cycles of evolution.
Opposite top A screen-shot of two creatures fighting over resources. Opposite bottom Another screenshot of competing creatures.
1994
Galápagos
1997
By Karl Sims Type : Interactive selection Galápagos is an installation of twelve computers simulating behaviours in abstract forms that float around the screens. With the help of step sensors, visitors can interact and apply pressure. In doing so, the program increases the fitness of the organism on the corresponding screen. Subjected to the dynamics of evolution, those with low fitness are discarded while the others are recombined in the next generation. As mentioned by Sims, this project could be seen as a tool to generate shapes, but also as a unique way of studying evolution.
Opposite The Galápagos installation with screens and corresponding foot pedals.
Electric Sheep
1999
By Scott Draves Type : Divided interactive selection Distributed over a network of computers, Electric Sheep is a screensaver of animated fractals initiated in 1999. Drawing inspiration from the novel Do Androids Dream of Electric Sheep by Philip K. Dick, Draves gives anybody who has downloaded the screensaver the possibility to create their own “sheep”, change existing ones, or become a “shepherd” and direct the mating process. The new animation is then added to the “flock”. The various creations are also breed together by the computers. Each user is invited to vote for the sheep they most enjoy and want to see used by the genetic algorithm next. The production of these animations are done using idle computers connected to the network which then distribute the new sheep all over.
Opposite A screen-shot of one of the evolving sheep.
Eden
2000 - 2010
By Jon McCormack Type : Ecosystem simulation Drawing inspiration from the Game of Life, McCormack developed an evolving acoustic ecosystem. Agents move around the world foraging for resources, producing sounds and occasionally mating together. In fifteen minutes, a whole year is gone in Eden, and the organisms have adapted to the changing environment, changing the sounds they emit. During their lifetime, the predators and prey learn about their world, and this information is then in part inherited by the offspring. With organisms reacting and even modifying the environment, the system is in perpetual dynamic change.
Opposite  The world of Eden, projected on perpendicular surfaces.
Genotyp
2004
By Michael Schmitz Type : Seeded evolution Schmitz created Genotyp, a project that weds genetics with typography. The interest lies in the possibility to produce offspring by combining two typefaces. To make this possible, Schmitz had to first solve a problem of compatibility and create a common language for all fonts (a common “DNA”). He decomposed existing fonts into three genes : the skeleton or fundamental structure, the ribs or thickness of the character, and the possible feet. With the help of a user interface inspired by evolutionary taxonomy, one can choose which typefaces to pair and follow the propagation of genes through the various results.
Opposite top Four screen-shots showing the software menu, and project explanation. Opposite bottom A screenshot of two evolving letters and their corresponding genotype.
Evolving Logo
2006
By Michael Schmitz Type : Divided interactive selection When designing the logo for the Max Planck Institute, Schmitz drew inspiration from evolution to produce the Evolving Logo. The institute works in the fields of molecular cell biology and genetics. The logo itself adapts to the state of the institute : first, certain variables in the establishment itself are used to generate samples and then the researchers vote for their preferred logo, hence creating a selection of “healthy genes” which will be expanded in the upcoming iterations.
Opposite top A successful variation used as the logotype for a period. Opposite bottom A collection of organism variations.
Colourfield
2009
By Jon McCormack Type : Ecosystem simulation Colourfield is an evolving landscape of colours. Through the simulation of an ecosystem, colour agents compete in a simple universe and produce colours based on their interactions with the environment (the other agents). With a cycle of feedbacks, Colourfield generates a palette of colours. Through co-existence and co-dependency, the system stabilises in harmonious colours until a more adapted variation forces all agents to evolve.
Opposite top A screen-shot of the evolving colour universe. Opposite bottom Several screenshots taken at different times.
Crystal Math
2016
By Karsten Schmidt Type : Interactive selection To produce random variation, Schmidt invited followers of Creative Applications Network to a website that recorded mouse and keyboard inputs. These inputs were then converted into digits that got fed into a GA to generate Barricelli-inspired simulations. The simulations were further refined and iterated with artificial genes. With the final pass of a shape generator, the outputs were manually judged and selected to appear in the final printed magazine : Holo 2.
Opposite  The final organism chosen to appear on the cover.
Conclusion Introducing variation into what is traditionally static leads to an obvious challenge of identity. Creating a logotype, for example, is no longer the problem of an ideal visual signature which is differentiated but varying traits which are recombined (Lynn, 2005). This challenge is suggestively analogous to speciation in biology. Indeed, it is clear that each living organism is unique. Still, one is capable of stating the similarities between organisms and group them into species of a kind thanks to the possible interbreeding of the individuals providing fertile offspring. The Evolving Logo project was about producing unique visual traits which, once defined, can vary without losing the “species’” overall unity. The introduction of EAs in computers has had a major impact on genetics, engineering, finances and architecture. Interestingly, the evolved solutions sometimes work in non-perspicuous ways which are yet to be explained. The model is, however, mainly used for optimisation. Using evolution for purposes which differ from sole optimisation raise quite a few difficulties which must be discussed further. Nevertheless, it is already clear that there is great potential in EAs and digital biology. As Bentley (2001) emphatically predicts : “These new techniques will form the next generation of our technology. By understanding the solutions of nature and using them to solve our own problems, we have found a whole new class
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of computation, a whole new way of using computers. Digital biology will allow us to survive in the modern world.” (p. 14) When well executed, these systems have the benefits of exploring larger solution-spaces and, in general, guarantee some kind of result. Moreover, these solutions are, by definition, adapted to the problem at hand, and these designs often exceed the designs of man in complexity, flexibility and performance. “It is therefore quite natural for designers to seek in evolutionary biology not only inspiration and metaphors but also research methodologies and algorithmic procedures for invention, problem solving and optimisation” (Pinkas, 2005, p. 3). It is reasonable to wonder : why have EAs on computers not taken over to a greater extent the process of design ? One important factor to note in the projects of the previous section is the varying reliance on the manual intervention of man. Is it possible to automatise these steps ? What are the difficulties that lie ahead ? Could one use EAs to produce autonomous self-evolving designs ?
Conclusion | 059
Complexity from simplicity
12. Daniel Dennett is an American philosopher and cognitive scientist who is best known for his book Darwin’s Dangerous Idea : Evolution and the Meanings of Life (1995). 13. The evolution of the eye was a long accumulation of variations that comprised the apparition of photoreceptor cells connected to nerve fibres. Followed by the folding of the surrounding area that limited the directional sensitivity, following variations slowly gave finer directional sensitivity with the creation of a pinhole. Transparent humor then developed in the chamber, followed by the first lenses. The final step involved the separate development of the iris and the cornea.
Introduction Popularized as an algorithm by Dennett,12 evolution is simple, yet powerful. One must only look at the complexity of the eye to understand this. The piecemeal progression of photosensitive cells to the complex vertebrate eye13 is a perfect example of small numerous gradations being profitable to its possessors. Although evolution by natural selection was discovered in biology, it would seem evolutionary processes extend far beyond the biological realm due to its algorithmic properties. Indeed, as I will try to show, the emergence of ideas in the human mind seems, to a certain extent, quite similar. In this chapter, one will see how evolution works and how it can be extended to other fields once generalised. Moreover, I will discuss the various disanalogies one may find when trying to implement evolution in digital designs, such as processing power, the representation of an environment, the limitation of resources, the definition of fitness criteria, the correct definition of the starting point, the unforeseen side effects and the manipulation of time.
Introduction | 063
Evolution extended In his book The Selfish Gene, written in 1976, Dawkins proposed to view the unit of natural selection, the gene, as a “replicator”. Simply, the replicator’s role is to make copies of itself, for the capacity to reproduce
Genotype
is the reason organisms exist. Moreover, the phenotypic expression
Phenotype
of the said replicator should be imagined as his “vehicle” or “survival machine”. This is what transports the replicator ; the throwaway machine which is born, faces the environment, and then dies. Its survival and effectiveness depends on what the replicator has pre-programmed into it. The dispute goes on between the gene-centric view of the modern synthesis of evolution and the extended evolutionary synthesis. However, recent discoveries help defend that the unit of natural selection is not solely the gene but the organism, shifting all explanations to an organism-centric view. This shift may impact various extensions of evolution, such as memetics and technological advancements : Memetics At the end of his book, Dawkins introduces a new term to explain that the idea of replicators is not linked specifically to genetics but can be generalised to any self-replicating unit of transmission. From the Greek “mimene” which means “to imitate”, the “meme” (inspired by gene) defines a unit of culture such as an idea, trend or fashion subjected to the same steps of evolution. Although no incontrovertible evidence exists to support the theory of memes, the field of memetics has grown… memetically. The transmission of knowledge has served human societies quite well ; mankind has been able to analyse challenges, produce solutions and therefore adapt to extreme ecological niches. This rise of culture and the acquisition of knowledge has become a fundamen064
Replicator Vehicle Above The extrapolation of genotype/ phenotype to replicator/vehicle. Opposite A table of the latest developments added to/changed in the theory of evolution (Laland, 2016).
Modern Synthesis
Extended Evolutionary Synthesis
The major directing influence in evolution is natural selection. It alone explains why the properties of organisms are adapted to match those of their environments. Genes are the only widespread system of inheritance. Acquired characters —non-genetic traits that develop during an organism’s lifetime— are not inherited and play no role in evolution. Genetic variation is random. Mutations that occur are not necessarily fitness-enhancing. It is mere chance if mutations give rise to features that improve the ability of organisms to survive and thrive. Evolution typically occurs through multiple small steps, leading to gradual change. That’s because it rests on incremental changes brought about by random mutations. The perspective is gene-centric : evolution requires changes in gene frequencies through natural selection, mutation, migration and random losses of gene variants. Micro-evolutionary processes explain macro-evolutionary patterns. The forces that shape individuals and populations also explain major evolutionary changes at the species level and above.
Natural selection is not solely in charge. The way that an organism develops can influence the direction and rate of its own evolution and its fit to its environment.
Inheritance extends beyond genes to include epigenetic, ecological, behavioural and cultural inheritance. Acquired characters can be passed to offspring and play diverse roles in evolution. Phenotypic variation is non-random. Individuals develop in response to local conditions, so any novel features they possess are often well suited to their environment.
Evolution can be rapid. Developmental processes allow individuals to respond to environmental challenges, or to mutations, with coordinated changes in suites of traits. The view is organism-centric, with broader conceptions of evolutionary processes. Individuals adjust to their environment as they develop, and modify selection pressures. Additional phenomena explain macro-evolutionary changes by increasing evolvability —the ability to generate adaptive diversity. They include developmental plasticity and niche construction.
tal drive, similar to that of reproduction in biology, so powerful it can affect biological urges. After all, homo sapiens is the only species capable of affecting its own reproduction rate because of belief.14
14. Certain religions require vows of celibacy and certain political regimes enforce one-child policies.
Susan Blackmore15 is a firm believer in memes. She (counter-intui-
15. Susan Blackmore is a British writer, psychologist and parapsychologist best known for her book The Meme Machine (2000).
tively) posits that the growth of the human brain over the millennia and the allocation of energy needed to support it are the effect of memes spreading through the population like a virus and trying to occupy more space in one’s mind (Blackmore, 2008). As with genes in biological organisms, these underlying patterns of linked neurons can mutate non-consciously to produce new memes (such as tunes, tricks, fashions, and fads). They are then expressed consciously and subjected to selection. If it is successful, the host may keep the meme and usually transmit it to others. Depending on the quality of the
Opposite A simplified view of memetic transmission.
communication, the meme may remain intact or change entirely. An individual’s experience (e.g. beliefs, education, culture...) will also greatly impact the mutation rate and selection of certain memes.16 With the recent development of internet memes, the intended meaning and definition of the original meme is being lost (Dawkins, 2013). Perhaps the term is simply undergoing its own evolution and adapting. Technology
16. The mistranslation of the Hebrew word for “young woman” in the bible into the Greek word for “virgin” is a perfect example of an evolving meme mentioned in The Selfish Gene (Dawkins, 1976). Of course, one may be surprised to find that —for reasons which will not be debated here— this mutation has been accepted and transmitted with success to a large part of the human population.
Darwinising non-genetic fields has been attempted by many. In The Evolution of Technology, Basalla (1988) detects evolutionary dynamics in the development of technology. He postulates that there are no leaps in the creation of new technologies, but that every discovery is the gradual amelioration and recombination of old ones and that their arrival at a certain point in time is inevitable. Before him, Augustus Henry LaneFox Pitt Rivers17 organised a whole collection of aboriginal artefacts by applying a taxonomy inspired by biological evolution. In doing so, it became clear that the steps of gradual improvement are also present in the creation of technology. Moreover, Basalla depicts the importance of varying environments, and how the context shapes the develop066
17. Pitt Rivers was a reconverted army officer who put together one of the biggest collections of anthropology and archaeology artefacts at Oxford.
Memetic transmission
Through communication, the meme is transmitted from host to host. Oftentimes the meme is mutated, and if successful, it is transmitted further.
Host
Meme Communication Vocal, written...
Mushroom club
Waddy boomerang
Bird club
Boomerang
Leangle
Throwing stick
War pick or malga
Lance
ment of technologies.18 One should note that the advances of science reciprocally influence the advances and developments in technology, just as in biology, the appearance of/or changes in one organism will affect all others in the environment. However, the analogy is not per-
18. As Basalla explains, wheeled transport was unknown to Mesoamerica prior to the arrival of the Spanish. However, even its arrival did not bear much influence due to the topographical features of the area.
fect ; discoveries in science are inflexible whereas changes in biological organisms can appear in some context, but then disappear in another.
Evolving artefacts From a common ancestor —the simple stick— have evolved various tools depending on their intended use. Depending on their successfulness, they were modified further.
Shield
Surprisingly, the conditions necessary for evolution to occur are minimal. These are : reproduction with variation, a selection mechanism which favours fitter organisms, and an environment with limited resources. Once these conditions are met, evolution must occur. The three steps of evolution, namely variation, selection, and inheritance, are then seemingly discernible in more fields than just biology. Following these analyses, one can more precisely define the terms (see following figure). Opposite A diagram showing the evolution of tools depending on their use and successfulness.
Evolution extended | 069
Evolutionary dynamics
Variation By variation, one understands the subtle or important mutations of the underlying information, which is named genotype in biology. This step is most often, though not necessarily, random.
Selection With the presence of fitness rules, each unit of transmission is rated, and attributed a fitness score (not numerical in biology) depending on how well they meet the fitness criteria and ensure the survival of the replicator. Selection, in all fields, happens at the phenotypic level.
Inheritance The underlying information or genotype of the fittest participants is then recombined (for sexual reproduction), or transmitted (for asexual reproduction) in the offspring of the next generation and the population is renewed. Once these steps completed, the algorithm begins afresh.
The process of design “It takes two to invent anything. The one makes up combinations ; the other one chooses, recognizing what he wishes and what is important to him in the mass of things which the former has imparted to him. What we call genius is much less the work of the first one than the readiness of the second one to grasp the value of what has been laid before him and to choose it.” (Valéry, 1949, quoted in Blachowicz, 1998, p. 380) When a designer claims to have an idea, is it the result of unconscious random variation or conscious directed variation19 and inheritance of previously selected ideas or memes that are then scrutinised consciously
19. The emergence of ideas may also happen through the use of scrapbooks, mind maps and other techniques.
in the mind ? A perhaps wild analogy would be that the mental representation of the idea would be the phenotype and the underlying map or connected pathways of neurons the genotype. The host will then consciously, although not numerically, attribute a fitness to the “organism”, allowing it to survive or discarding it. The organism is then realised in the physical world as prototypes, which are tested, and the mind of the designer selects the fittest based on the feedback and initial desires. Variations are thought of to improve the existing concoctions, applied, tested anew and so on. The three conditions for evolution to take place are present : there is reproduction of ideas with variation, a designer who favours certain productions, and limited resources such as time, the materials and techniques used, and financial amounts allowed. The process of design can sensibly be seen as evolutionary. Through the ages, it has been manually executed. Nevertheless, with the arrival of computers, the designer has been able to delegate to varying extents the various steps to help in the production of designs and even, more generally, in the emergence of ideas. Depending on the Opposite A diagram showing the three steps of the algorithm for evolution.
The process of design | 071
Design dynamics
Design
Evaluate
Prototype
machine’s responsibilities, I would categorise the processes thus : Traditional design It would seem that the talent of a traditional designer lies in the ability to best project the phenotypic expression of one’s idea into the wanted medium. Essentially, one must manually execute the steps of morphogenesis to produce the design in the physical environment. One might express the information verbally, visually or otherwise ; basically translating20 the idea into the perceptive realm. As such, the designer projects from the non-physical to the physical. In the traditional process, the steps of evolution are brought about by the
20. Not to be confused with genetic translation ; here one understands the process of externalising an idea in one’s mind into the perceptive realm.
designer. Indeed, when creating a new logotype, one would attempt to best recreate the envisioned solution by manipulating graphical tools mimicking real ones (such as pens, erasers, and compasses). He then explores his production further by making small variations to what already exists. Through the manual process of trial and error, the designer iterates and eventually arrives to a satisfactory logotype. Generative design In the case of generative design, one must improve skills of abstraction,21 extracting general rules from specific examples. One attempts to translate into computer code the process of morphogenesis and creates a set of variables that act as the genotype. The computer then projects
21. By abstraction, one understands the process of uncovering and defining general rules from a series of specific simple examples.
the phenotype through the predefined process with great speed, which allows the designer to explore a greater set of prototypes than could have been done manually. For generative design, the variation is executed by the computer. Selection and inheritance however, are still contributed by the designer. For example, the creation of a logotype demands of the designer not to manually draw it, but to describe it to the computer through code. By introducing randomness into the description, the computer proposes a great range of productions at Opposite A diagram showing the three steps of the process of design.
The process of design | 073
great speed. Through the use of parameters, one can adjust the logotype further to all necessary mediums. Interestingly, the results due to bad variations (mistakes in programming from the designer) start a dialogue between man and machine which can help the emergence of ideas, as I noticed whilst researching for an earlier paper (Donaldson, 2014). EAD by computers Finally, when talking about EAD by computers, one must first abstract and then translate the process of morphogenesis, similarly to generative design. With the extracted variables and algorithms, one must then transcribe ;22 convert the relevant variables down to a common language, roughly equivalent to DNA. With transcription, one must define what code is run for which sequence of the common language, analogically to which proteins are synthesised from DNA. Lastly the designer must program and schedule the three steps of evolution. The integration of evolutionary dynamics will then automate the design process —to a certain extent. This is where EAD by computers is implemented between two extremes : namely, 1) human/ tool symbiosis, which is essentially an interactive selection ; and 2) tool/artefact autonomy which is simply an ecosystem simulation. Across the spectrum, there is a wide range of possibilities where man and machine share the three evolutionary processes ; these will be further explained in part one of chapter four. It might seem not such a far step to imagine a tool to evolve designs, or further, autonomous self-replicating designs, considering that the requirements and process of evolution are —conceptually at least— quite simple. Nonetheless, the reality of it is quite different. Correctly emulating the complexity of evolution on computers is a challenge that requires much thought.
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22. Not to be confused with genetic transcription ; here one understands the conversion of numerical variables (e.g. integers, floats) into a common language (e.g. bytes). Opposite A commented decomposition of the steps included in the three categories.
Traditional design
Generative design
EAD by computers
Translation
Abstraction
Abstraction
Externalisation of an idea in one’s mind into the perceptive realm.
Uncovering and definition of general rules from a series of specific simple examples.
Uncovering and definition of general rules from a series of specific simple examples.
Translation
Translation
Externalisation of an idea in one’s mind into the perceptive realm.
Externalisation of an idea in one’s mind into the perceptive realm.
Transcription Conversion of numerical variables (e.g. integers, floats) into a common language (e.g. bytes).
Evolution Integration of the evolutionary dynamics : variation, selection, and inheritance.
Human/Machine responsibilities
Selection
Human
Inheritance
Human
Variation
Human
Innovator
Human — Machine
Human
Machine
Traditional design
Machine
Machine
Human
Machine
Machine
Human
Human
Machine
Generative design
EAD by computers (interactive selection)
Machine
Human — Machine
EAD by computers (ecosystem simulation)
In vivo versus in silico “Barricelli saw his computer organisms as a blueprint of life —on this planet and any others. ‘The question whether one type of symbio-organism is developed in the memory of a digital computer while another type is developed in a chemical laboratory or by a natural process on some planet or satellite does not add anything fundamental to this difference,’ he wrote. A month after Barricelli began his experiments on the IAS machine, Crick and Watson announced the shape of DNA as a double helix. But learning about the shape of biological life didn’t put a dent in Barricelli’s conviction that he had captured the mechanics of life on a computer. Let Watson and Crick call DNA a double helix. Barricelli called it ‘molecule-shaped numbers.’” (Hackett, 2014) Advances in the study of evolution have demonstrated how natural selection is more complex than Barricelli predicted. The switch from a gene-centric view to an organism-centric one has kept the underlying fundamentals, but they now rest on other assumptions. When passing to the digital realm, one must consider the various difficulties and benefits that accompany the transition. Indeed, possible hurdles may appear in the new universes created by designers such as 1) the capacity of computers and man to handle the EA, 2) the simulation of relevant environmental interactions and scarcity of resources, 3) the correct formulation of fitness rules and their relative importance, 4) the judging of goodness, 5) the appropriate representation of the origin of the universe and 6) the potential for unforeseen side effects. However, the added possibility of 7) digital time manipulation perhaps allows one to spot relatively quickly and manage these difficulties. I will now examine these points one at a time :
Opposite An explanatory table of human/machine responsibilities.
In vivo versus in silico | 077
Processing power Since the 50s, mankind is witnessing the computing revolution. The power, speed and miniaturisation of computers are increasing exponentially. So much so that the will of scaling smaller is reaching its physical limits.23 But as powerful as computers are today, they are not powerful enough (yet ?) to take on the simulation of a realistic universe. The multidimensional complexity of the world is such that emulating it on a computer asks for unavailable resources. Even so, science does not (yet ?) comprehend all the rules and variables at play. Depending on the problem the designer is considering, it may sometimes be more appropriate to select what behaviours and characteristics of evolution must be present in the digital universe and which not. Otherwise, for complex systems bearing many fitness dimensions, the selection calculations of even just one organism could take up days of computing and perhaps more. Similarly to the limitations of computing power to execute the selection calculations, a designer or user would have an equally hard task if he had to browse the hundreds of generated organisms and manually evaluate their fitness level. To overcome this issue, certain projects such as Electric Sheep (Draves, 1999) or the Evolving Logo (Schmitz, 2006) rely on (a massively) interactive man/ machine selection, involving hundreds of human minds to collectively judge and attribute fitness scores to the organisms. Environment & resources Natural selection is the product of innumerable interactions between organisms and their organic and inorganic surroundings. In essence, the environment is an ever-changing landscape where slight changes in one species will ripple through the environment forcing slight changes in others. The geophysical world (e.g. rap078
23. Transistors now approaching the size of atoms cause a problem known as quantum tunnelling, rendering them non-functional. The next step involves the use of quantum superposition in “Qubits” to store information in a particle’s polarisation (Kurzgesagt, 2015).
id or wide currents, steep mountains, deserts...) can also direct change. Indeed, as Darwin points out, the movements of rivers, seas, oceans, mountains (even though the displacement of the earth’s crust, the tectonic plates, was unheard of in his time) and the weather play a major role in the evolution of species (Darwin, 1859).24 It has also been discovered that organisms have a certain plasticity in development which permits adaptive evolvability. Depending on the environment and the presence of resources, the same genotype
24. Darwin made extensive experiments with the seeds of plants drowned in waters of various salinity and came to the conclusion that, even after long periods of time, the seeds can still be planted and grow. Which also provides an explanation for the similarity of flora on different continents.
may produce various phenotypes, a sort of evolutionary process taking place inside an organism (Laland, 2016).25 The phenotype chosen for its better adaptability will then affect the genotype of the
25. Marine sticklebacks will grow differently in response to food being either mid-water or on the bottom surface.
individual and in turn shape the evolution of the whole species. Scarcity of resources creates predation, competition and sometimes cooperation between all living organisms ; generating a predator-prey relationship, a competitive relationship —which can be intra or interspecies—, and an altruistic one, making the environment more or less hostile. Altruism can be expressed mathematically thanks to William Hamilton (1964) :26 if the relatedness between two genotypes multiplied by the benefits in reproduction of the recipient is bigger than the cost in reproduction of the individual that would perform the act, then the act is performed.27 It is questionable whether such plasticity in development, ontogeny, or competition between organisms —even if successfully modelled— are vital in EAD by computers. A designer could either
26. William D. Hamilton was an English evolutionary biologist who became famous with his explanation of altruistic behaviours and kin selection through a simple mathematical equation. 27. Certain animals will perform warning calls for the good of their close relations putting themselves at risk by divulging their position to predators. Birds will sometimes forgo their own reproductive opportunities to help raise the offspring of other close relatives.
evolve designs through ecosystem simulations, movements in a space coupled with simple fitness rules (as for McCormack’s Eden, or Sims’ Evolved Virtual Creatures), or through complex fitness calculations –which are actually a distillation of the ecosystem’s interactions. As mentioned before, perhaps acting somewhere along this spectrum would produce the most interesting and promising results.
In vivo versus in silico | 079
(In)organic interactions
Organic/inorganic components will interact with their organic/inorganic surroundings for resources, creating an intricate web of countless interactions.
Organic Inorganic
Organism feedback
The genotype affects the phenotype that in turn affects the environment, this is also true the other way around : the environment affects the phenotype, and in turn the genotype.
Phenotype Organism
Genotype Environment
(Organic and inorganic)
Fitness rules and weights In Climbing Mount Improbable, Dawkins (2006) presents the reader with an imaginary geographical landscape as a metaphor for evolution : Mount Improbable. Within this mountainous region, one may only ascend, never descend. It symbolises natural selection, which is only able to select better adapted organisms, forcing the fitness to go higher, never lower. Each peak is an accumulation of adaptations which results in the development of traits such as wings, cornea, photosynthesis and the like. Inevitably, certain fully developed traits can be inferior to other fully —or even partially— developed traits according to some objectively quantifiable measure. Indeed, the evolution of visual organs can lead up various paths based on what type is better adapted to the environment but the path taken by evolution is not always the best for all environments. When a species has climbed up such a peak, it is stuck in a local optimum. It seems to be true that species stuck in local optima may never leave it, condemning them to never fully develop the global optima of a branch of traits. This is due to the path leading to such “lower” adaptations bearing the same (or sometimes higher) attractiveness and thus natural evolution favouring it.28 Certain modern computer algorithms are allowed to accept worse solutions than a current best in order to avoid this problem. With finite resources, the computer must sometimes take one step back before it takes two steps forward (Rice, 2013).
28. The existence of the pharynx in mankind, a passage used for both ingestion and respiration, is an evolution which increases the risk of choking ; a division of both passages would have been a better adaptation than the current one.
To bring this back to media design, one could consider the evolution of artefacts that control computers at a distance (Bolli & Nova, 2014). Indeed, the various joysticks, keyboards, gamepads, and remotes that man has designed over the years has, at several occasions, climbed to local optima. For example, the paddle29 and Game & Watch30 have reached such a point in the given context and thus their evolution has reached an end point. Luckily, it is easier for man to realise such stagnation and abandon such peaks of evolution to explore others. Opposite top A diagram showing the numerous interactions between organic and inorganic components of the environment.
Opposite bottom A diagram showing the feedback taking place between genotype, phenotype, and the environment.
29. Famously used with Pong, the paddle is a game controller with one centre wheel and several buttons. 30. Built with one LCD screen and a few buttons, each Game & Watch is a hand-held electronic game with pre-installed software.
In vivo versus in silico | 081
The eye region of Mount Improbable
Corneal eyes of land vertabrates
Intermediates Debris-copepods Superposition eyes
Fish eyes
Neural superposition Apposition
Cephalopod Lens-eye
Mirror eyes Tapetum ridge
Spiders Limulus
Mere photoreceptor
Pigment cup eyes
Proto-compound eye
Vitreous mass eyes Nautilus
Near-pinholes
Reflecting pigment cups
Natural selection has, through the interactions between living and non-living things, given rise to a number of fitness criteria that cannot be accounted for when trying to emulate it on computer. Indeed, the evolution of the ecological relationship of organisms with their ecosystem through predatorial or symbiotic relationships produce a complex interconnected web of relations.31 For EAD by computers, one must scale the amount to an acceptable size. Designers are then responsible for the creation of fitness rules and their respective importance. The danger here may be the injection of arbitrary fitness pressures and weights. Indeed, it may be impossible for designers not to be biased
31. Through the illustration of food webs, one can quickly grasp the exponential growth of connections between all living forms that feed, are feed on, at different levels and how the disappearance of a single species would greatly impact the system.
when defining how selection works, unintentionally favouring the wrong criteria and introducing subjectivity, which is impossible in nature. This may not be an issue when the solution space is already clear or when the designer knows precisely what outcome is warranted ; nevertheless, when it is not, the tool or design may be prone to problems such as stagnation in local optima. Surely, if a designer believes that a greater x-height32 would provide a better readability for his evolving typography, he may overlook the better combination of kerning33 and stroke,34 thus never attaining the “best” readability for his project. With today’s extended evolutionary synthesis, the gene-centric nat-
32. The height of a typeface’s lowercase letters (disregarding ascenders and descenders) that rest on the baseline is equal to the x-height. 33. Kerning is the horizontal spacing between two letters. 34. A stroke is known as the main diagonal part of a letterform.
ural selection is no longer considered the sole reason for all adaptations found on earth. It would seem that the direction of evolution of an organism is also determined by its own development and choices. Individuals will adjust to their environment during ontogeny and modify selection pressures in doing so (Laland, 2016).35 Effectively, this changes the attractiveness of certain paths up Mount Improbable. In addition, the landscape is ever changing due to variations in the organic and inorganic environment such as weather
35. When building nests, birds are reducing the selection criteria for physiological regulation of egg temperature and thus augment the weighting of the nest design fitness criteria.
conditions and geological changes. EAD by computers would provide designers with the tools to sculpt the landscape, or, change the fitness profiles36 in whatever way the designer wants. But could
Opposite A section of the hypothetical Mount Improbable for the evolution of visual organs.
36. The profile of a fitness rule is the resulting curve plotted on a two-dimensional graph with one axis for the fitness, and one axis for a specific criterion.
In vivo versus in silico | 083
evolving designs impact their genetic pool in any meaningful way if they do not sense a situation in which their survival is simplified ? Design goodness As seen, EAs successfully tackle problems autonomously in the fields of engineering or logistics. However, in media design, the issue is quite adverse. The problem lies in the unquantifiable measure of aesthetic quality. Indeed, the governing fitness values in optimisation problems in engineering for instance, are calculated thanks to one numerical value (e.g. power output). For media design, searching to create a fitness rule that selects a more “beautiful” shape is an ill-conceived endeavour, if not an impossible one. Dutton (2010), in his talk a Darwinian Theory of Beauty, explains how man’s concept of beauty is one that has evolved over the millennia. Indeed, Dutton argues that what is aesthetically pleasing to humans relies heavily on what is good for them.37 However, as Dawkins remarks : “To simulate natural selection in an interesting way in the computer, we should forget about rococo ornamentation and all other visually defined qualities. We should concentrate, instead, upon simulating nonrandom death.” (1986, p. 88) When programming the process of morphogenesis for an EA, the designer will already include aesthetic taste, choosing to produce phenotypes of a certain colour, size and shape. However, this does not solve the problem ; it is still unclear how the process of selection could judge a solution to be more aesthetically pleasing than another. The tool DesignEye, surprised its creators (Rosenholtz, Dorai & Freeman, 2011) when users desired it to have an objective measure of the “goodness” of a percept. The users were essentially looking for an “oracle” capable of settling debates about best designs. This was not the intent however, for the tool is merely an algorithm that tries to display the perceived saliencies of inputted images and cannot 084
37. The attraction of symmetry, for example, is one that is explained by the search for mates with healthy genes to produce healthy offspring. Which foods one finds attractive is also dependent on how nutritious the foods are. Certain landscapes are more aesthetically pleasing for the higher ease of survival they provide.
define how good the image is. For now, it is still impossible to imagine the complete objectification of such data and the removal of manual selection in some part of the process. A potential “best case scenario” would possibly implement known rules of aesthetics (e.g. symmetry), but still wait for the conscious direction of the designer. Starting point According to contemporary physical theory, our universe is built on four fundamental forces (strong, weak, electromagnetic and gravity) that dictate the way particles interact with each other. These particles (such as quarks, leptons, and bosons) are the building blocks of everything encased in our four dimensions (the three spatial dimensions and time). All living things have evolved using the same four nucleobases : adenine, cytosine, thymine, and guanine (which are a precise combination of stable particles). After that, genomes are simply long strings of intertwined base pairs. These humble beginnings are nonetheless the origins of life and its tremendous diversity. So where should an EA begin ? Oftentimes, engineers and designers begin their digital evolution mid-course, assuming the previous steps of adaptations. Analogically, an all-seeing deity might design reality, all life on earth, and perhaps create two living forms in his image so that they could enjoy a relationship with him. One might even give them names. In the case of engineering, this approach may be of use for situations where the initial conditions are clear, yet for a truly artificially evolved environment, starting halfway up the mountain restricts the very potential of EAs and thus might never discover better solutions.38 On the other hand, starting from the very early building blocks of life may lengthen the search so much that it cannot be considered a viable option. Where should one draw the line ?
38. Quite often, engineers apply GAs to the optimisation of a specific piece when perhaps the biggest improvement would lie in the overall design.
Is there any milestone along the path which would be a good starting point ? Obviously, such decisions depend on the project at hand and its context. The amount of time available, the necessary resources and In vivo versus in silico | 085
other restrictions will lead to the definition of the starting point. Computational biologists Chris Adami and Charles Ofria may have found a solution to this problem. When creating their computer program Avida,39 they made use of EAs to solve complex problems. “In one experiment, the Avidians were set to the task of evolving the ability to solve a complex logic problem [...] Only four out of the fifty digital
39. Avida is a open source software which conducts experiments on self-replicating computer programs which evolve to solve various logic problems.
populations evolved the code necessary to complete the operation. All of the successful populations were ones that initially carried a lot of mutations (random pieces of computer code) that made it harder for them to solve math problems and therefore reproduce. [...] bad mutations were essential to improving the fitness of later generations, perhaps because they added to the genetic variation that later random mutations
Opposite A screen-shot of an Avidian population running in the console.
could act upon.” (Zorich, 2014) It may be that if one designer wishes to “jumpstart” the evolutionary process, he must introduce a higher rate of mutations as well as a predesigned possible solution, allowing the algorithm enough space to step back down a peak. Such variation provides a richer set of possible behaviours, making the designs more adaptable. Unforeseen side effects In computing, confinement40 is useful for testing variable parameters and controlling the generated outputs. However, this may limit the way
40. By confinement, one should understand the restriction of interactions in a system to a minimal size.
one solves a particular problem and, more often than not, the designed solution works only for that confined space. In nature, this limitation is rare. Had the populations in nature been constricted thus, the endosymbiosis of early cellular life forms may never have taken place, and eukaryotes would never have appeared.41 Accordingly, genetic code is blended in all kinds of ways. This constant renewal of information allows for unexpected side effects and original solutions to difficult problems. As mentioned earlier, the most notable advances in technology arose through the recombination of old techniques. It is therefore clear that confinement may not be the answer when developing GAs. Even if the digital organisms are intended for a particular medium, 086
41. The endosymbiotic theory describes a prokaryotic cell engulfing an aerobic bacteria (a bacteria which converts oxygen into ATP). Due to the benefits of the bacteria in the production of energy, the host cell would not have digested the bacteria. This intruder is now known as the mitochondrion. It is also believed that chloroplasts appeared through a similar process with algal cells.
they should not be restricted in their ability to crossover genetic information. One might then consider the creation of a common language allowing, for example, the merging of radically divergent genotypes. In nature, the level of confinement is directly linked to the diversity of species. On small islands, the available space and resources force nature to optimise the few species and leaves no room for speciation. On the other hand, bigger islands provide nature with enough space and resources to experiment and explore every environmental niche, increasing diversity and allowing more genetic variations. For EAD by computers the “allowed” space should depend on how sure the designer is of the path he would like to explore. Tempus fugit The disanalogies between nature and silico that I have reviewed may not all be disadvantageous. Obviously, the changes in biology are only visible over very large time periods. This is where the power of modern computers will come into play : time manipulation. Nothing at all would ever change —let alone evolve— without the passing of time. For mankind, time cannot be manipulated in any way. Of course, one could consider the laws of time dilation to accelerate or decelerate the growth of an organism by accumulating velocity in the same way that astronauts currently orbiting earth are actually 0.0085 seconds younger after a year spent in the international space station. Luckily for the research of this paper, one need not go to space as time travel can be programmed into our virtual universes, giving us the power to calculate at a much faster pace and rendering it possible to generate hundreds of iterations in very short time spans. One may also go back in time if one is not satisfied with the outcome and wishes to favour a different path. Consequently, this becomes an effective testing mechanism to resimulate the EAs and observe their conver088
gence42 or contingency43 (Zorich, 2014) ; that is, whether rewinding time and rebooting evolution would inevitably harness the same results in time, or whether the probabilistic appearance of certain events would force a completely divergent path. By harnessing these powers of virtual time manipulation, it would be possible to add what is normally forbidden to Dawkins’ Mount Improbable : downhill travel.
42. Simon Conway Morris thinks that over time, natural selection leads organisms to evolve a limited number of adaptations to the finite number of ecological niches on earth. This causes unrelated organisms to gradually converge on similar body designs. 43. According to Stephen Jay Gould, the existence of animals, including us humans, was an event so rare that it wouldn’t re-occur if we rewind time to the Cambrian period and start again.
In vivo versus in silico | 089
Conclusion Once genotype and phenotype are extended to replicator and vehicle, evolutionary processes seem to be uncoverable everywhere. Even this paper is the result of variation, selection and inheritance. Indeed, each passage, receiving criticism, was either kept and varied or discarded altogether. However, their implementation in the digital is far from simple. As mentioned earlier, recreating complex systems on the limited power of contemporary computing and current limited knowledge are major challenges. So much so that they are the main probable reason for the lack of presence of computer assisted evolution in design (cf. supra p. 27). A combination of both digital evolution and conscious direction from a designer as in the examples discussed seems to be an effective way to harness the power of evolution (the same as genetically selected organisms (GSOs) by humans in flora and fauna).44 Of course, the designer would be able to intervene at any time. This should effectively improve search capabilities and avoid the dragging of local optima. One must nonetheless plan how much of the evolutionary process can be delegated to the machine.
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44.  By visiting a market, one essentially sees thousands of years of GSOs by man. The fruit and vegetable available have been chosen and breed further to produce the most nutritious resources.
Writing the genetic book of the dead
Introduction Successful EAD by computers demands a careful, well thoughtout, division of labour between man and machine. Considering the aforementioned benefits, difficulties, necessary resources, and relevance, one must judge the best course of action to be taken. However, the problematic definition of aesthetics will remain a stone unturned, forcing the evolution of digital universes to still rely on the intervention of man to some degree. I will now discuss the extremes and range of possibilities between a human/tool symbiosis and a tool/artefact autonomy. I will also attempt to provide a general structure which should help media designers to implement evolutionary processes into their projects.
Introduction | 095
The spectrum of EAD by computers
Autonomy
Synergy
Symbiosis
Tool/artefact independence
Human/tool/artefact (in)dependence
Human/tool dependence
Divide ut regnes In the case of a human/tool symbiosis, the computer handles variation and inheritance while the designer handles the selection process (as for Dawkins’ Biomorphs). This extreme takes advantage of the computer’s speed on one hand and the talent and intent of the designer on the other. However, the limitation of such a system resides in the profusion of organisms to be (manually) judged piecemeal, rendering it ineffective for problems with sizable search-spaces. One can, in some cases, turn to divided interactive selection to resolve this issue (cf. supra p. 80). For the tool/artefact autonomy case, one must define the rules of selection in code to allow the system to evolve independently (as for Sims’ Evolved Virtual Creatures). Thereafter, the process of digital evolution may go on unhindered by man’s slowness. Considering there is no effective procedure to determine whether something has the property of beauty, it may be that such autonomous systems will not see the light of day anytime soon. Perhaps fully autonomous designs would involve the evolution of independent aesthetics in the digital universe which may not align with man’s ; creating their own rules through time of what is considered beautiful by the “organisms” and what is not. However, this would hardly be a useful tool for designers and would rather fall into the category of scientific exploration (to try and understand how aesthetics evolve among organisms by reproducing it). Eden, by McCormack, faces this exact problem. As an ecosystem simulation that produces sounds, it comprises organisms which move around at certain speeds, looking for resources, and fighting for survival. The selection process here is based on these characteristics (e.g. mobility and strength), and certain audio expressions are tied to these traits. The fittest organisms are then the ones expressing their sounds most often. However, the outputted concert may not be aesthetically Opposite A diagram showing the dimension of dependence of a design in EAD by computers.
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pleasing, and rightly so, for it is not the various sounds that define the characteristics of fitness but the characteristics that define the sounds. Other possibilities, and the most likely to make advances, lie in between these two extremes. One may divide the various tasks differently providing more or less leeway to man or machine. This would most likely depend on the assurance of the designer. Depending on the complexity or simplicity of the project/design, the designer could control more steps, ensuring its correct evolution and handling exceptions along the way, or, let the computer program run its course, guaranteeing a fast evolution down the correct path with precisely set up rules. At the beginning of a project, one could imagine using a more controlled system, heavily reliant on the designer. In doing so, the designer could reflect on his choices and analyse them to uncover the fitness trends. One could record and analyse the data of the organisms he deems most fit to uncover hidden relations. Once the abstract rules uncovered, one would then best implement them and impart more to the machine, slowly extracting oneself from the evolving universe. Say one would like to evolve typography ; with so many variables at play an EA would greatly aid in such a task. To commence, the designer may input existing fonts (like for Schmitz’s GenoTyp project), or, start with simple grid structures of connected points. From there, the system may run a number of generations providing a diversity of organisms that the designer then judges. From his choices, the various relations between stem height and thickness, terminal radius, aperture wideness, and shoulder height could be graphed and perhaps certain rules abstracted. The next generations would then provide a more controlled range with more interesting variations. The designer could then repeat the process over and over. With the aid of machine learning in data mining problems, it may be that extracting rules of selection could be greatly improved. Evidently, a good division of labour would exclude any hindrance from both parties (avoiding the processing slowness of man and the 098
incomprehension of human preferences, aesthetics, and other by computers). I would then propose a solution involving a continuously evolving universe, independent of man, with interactions that allow the designer to steer the population in real-time (by means of interactive selection, specific variation and controlled inheritance). Given the wide range of choices in each step of a GA (e.g. mutation rate, crossover probability, mate selection), such an approach would : Allow the designer to provide or remove fitness rules and adjust them over time, thus refining the selection process and increasing independence. Provide the creator with an ultimate fitness scoring ability which overrides the score set by the computer, thus making it easy to keep or discard any phenotypic traits and their corresponding genotypes. Essentially, the designer could carve the mountains and valleys of the fitness landscape, making the system easier to steer. Allow the input of precise genetic makeups, thus aiding the algorithm with the talent and intent of the designer. This would greatly help with systems stuck in local optima for example, granted the designer notices the stagnation. Allow specific parent selection for next generations, perhaps creating unexpected results that would not emerge autonomously. Such control could help create new search paths in the space, granted the resulting fitness is good enough.
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To further demonstrate a plausibly ideal method, I invite the reader to consult the section entitled “Shaping Mount Improbable” (cf. infra p. 125) in the annexe that contains a proposed method to implement evolution in designs. However, “in the real world, most design involves not individual designers, but teams that must collaborate across several disciplines : design, engineering, marketing, and product management.” (Rosenholtz, Dorai & Freeman, 2011) It would be most interesting to create such a tool for evolution and view how it adapts to multiple inputs and divergences of opinion in a team.
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Conclusion EAD by computers relies on a careful division of labour. As seen in the various projects of the first chapter, one may impart more or less to the machine. Each extreme suggests certain issues which would render the use of EAs either heavily dependant or close to irrelevant. The sweet spot resides somewhere in between, taking advantage of computing speed and the guidance of human intuition and aesthetic taste. The method proposed must be tailored for each individual project. Indeed, certain variation, selection or inheritance methods may not be necessary. However, the overall structure remains the same : define the solution space, determine the major parameters, create the search space, frame the design space and expound the design constraints (cf. infra p. 125). It is then up to the designer to act as a caretaker and ensure the desired evolution.
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Future horizons
Introduction Imagine a world where logotypes, typographies, music, and other media are digital organisms which, evolving in their own reality, perceiving the world through sensors, can adapt to any situation following the laws implanted by the designer. Imagine whole universes of sound organisms forever mutating, inheriting, and better adapting to the wants and fancies of the designer, providing him with tailored solutions for his project. Where artboards evolve to the preferences of the artist. Where fonts evolve to the greater clearness, comfort, and speed of the reader. Imagine media designers as fathers of creation ; generating universe after universe, each teeming with digital life. Imagine them as watchful guardians providing for the various A-life forms. One would be able to control the fate of such worlds, instilling the “meaning of life” and “purpose” of the inhabitants. One would evolve worlds providing intelligent, performant, and adapted solutions to each problem or idea.
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Not without man Evolution by natural selection is nature’s most powerful problem solver. Through the simplicity of variation, selection, and inheritance are born the most fascinating and complex of living organisms. Through the smallest set of conditions : reproduction with variation, limited resources and a selection of the fittest, organic life seems to always find a way to adapt and replicate. The existence of evolutionary processes can be hypothesised in non-biological fields once the replicators and vehicles are defined. Even the process of design can be seen as the result of an evolutionary process where the designer will design, prototype and evaluate. Evidently, in such a field, the process of evolution is brought on and directed by man. It is then quite clear that using one of our most powerful technologies, computing, could prove useful in the evolutionary process of design. It is indeed tailored to mechanically execute algorithms quickly and without error. This is a core fact of evolutionary design by computers (Pinkas, 2005). Even though the potential benefits of applying evolutionary dynamics to the design of media organisms with computers seems fruitful, it is no small task. Certainly, the difficulties in emulating evolution in computers make all current attempts a far cry from reality. Even though computers may allow for interesting time manipulations, as I have argued, problems such as computing power, man power, the simulation of environments and resources, the correct weighting and defining of fitness rules, appropriately stating the starting point and allowing for unforeseen side effects make EAD by computers a difficult task. Moreover, the algorithmic definition of design aesthetics or “goodness” in fitness rules is perhaps a nonsensical task that implies the essential presence of the designer. With the sceptical nature of many designers who fear to have their intuitions replaced or constrained, I believe this to be a good thing.
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Potential solutions may lie between the extremes of human/tool symbiosis and tool/artefact autonomy. A well-thought division of labour of the evolutionary steps between man and machine would take advantage of their respective strengths and slowly compensate for their respective weaknesses. Indeed, by gaining understanding and assurance in the project at hand, the designer will delegate more to the machine, earning in speed and precision. Most likely, a powerful framework would involve a system evolving in real-time that encompasses designer interactivity. The designer could manually vary, select or control inheritance, acting as a watchful guardian leading the universe to desirable outcomes. Autonomous digitalia seem utopian today. Nevertheless, advances in science, design, and computing are bringing these imaginary organisms closer. And rightfully so, one should strive for EAD by computers if one wishes to produce adaptable, versatile, customisable, optimisable and self-correctable designs. Moreover, these endlessly searching media universes gain in speed and independence providing the designer with a creative discussion and introspective learning of one’s aesthetic tastes. However, with their arrival various changes are guaranteed : For the designer, EAD by computers will demand a shift of focus from producing specific, constrained solutions or procedural designs to producing general, interactive systems, combining varying traits which will evolve to the better adapted solutions under the guidance of a watchful guardian. For the user, which was not discussed in this paper, a renewed education of product use where one must not adapt to the design but learn how to adapt the design to the user. For the market, also not discussed, creating designs which could continuously evolve with the user, avoiding the purchase of a newer version, would possibly force the search of a new economical model. Not without man | 107
Forgive me father for I have sinned ? As an afterthought : A-life and AI are growing ever more useful and life-like. Invading our homes and our technologies for certain daily tasks and even replacing jobs. Some may believe, as Blackmore (2008) does, that mankind will soon be facing the next extinction threatening event with the arrival of technological replicators (temes). To the same accord, Harris45 (2016) warns about a certain failure to intuition and that “the gains we make in artificial intelligence could ultimately destroy us”. To him, there are no guarantees that the goals of AI stay aligned to the goals of mankind. Harris plainly admits that he has not thought of any solution, only that discussion on the topic should be started. Perhaps this paper is also a failure to intuition, as he describes, and contributes in its own way to the possible annihilation of mankind. I certainly hope this is not so.
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45.  Sam Harris is a neuroscientist and philosopher whose work concentrates on the evolution of understanding and how it may impact the way humanity lives.
Annexes
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>> Rice, S. (2013). Evolution in a Finite World. [online]. Access : http ://nautil.us/ issue/7/waste/evolution-in-a-finite-world >> Schmitz, M. (2006). Evolving Logo. [online]. Access : http ://interaktivegestaltung. net/evolving-logo-2/ >> Schmitz, M. (2004). GenoTyp. [online]. Access : http ://interaktivegestaltung.net/ genotyp/ >> Sherman, P. & Seeley, T. (2009). Animal Behaviour. [online]. Access : https ://www. britannica.com/topic/animal-behavior/Function#ref1043131 >> Sims, K. (1994). Evolved Virtual Creatures. [online]. Access : http ://www.karlsims. com/evolved-virtual-creatures.html >> Sims, K. (1997). Galápagos. [online]. Access : http ://www.karlsims.com/galapagos/ >> Zorich, Z. (2014). If the World Began Again, Would Life as We Know It Exist ? [online]. Access : http ://nautil.us/issue/14/mutation/if-the-world-began-again-wouldlife-as-we-know-it-exist
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Videos >> Bawazer, L. TED Ideas Worth Spreading. (2013). Genetically Evolved Technology. [online video]. Access : http ://tedxtalks.ted.com/video/Genetically-EvolvedTechnology >> Blackmore, S. TED Ideas Worth Spreading. (2008). Memes and “Temes”. [online video]. Access : http ://www.ted.com/talks/susan_blackmore_on_memes_and_temes >> Dawkins, R. TED Ideas Worth Spreading. (1991). Growing Up in the Universe. [online video]. Access : http ://www.ted.com/talks/richard_dawkins_growing_up_ in_the_universe >> Dawkins, R. Saatchi & Saatchi. Youtube. (2013). Just For Hits. [online video]. Access : https ://www.youtube.com/watch ?v=GFn-ixX9edg >> Dutton, D. TED Ideas Worth Spreading. (2010). A Darwinian Theory of Beauty. [online video]. Access : https ://www.ted.com/talks/denis_dutton_a_darwinian_theory_of_beauty >> Harris, S. TED Ideas Worth Spreading. (2016). Can we build AI without losing control over it ? [online video]. Access : https ://www.ted.com/talks/sam_harris_can_ we_build_ai_without_losing_control_over_it ?language=en >> Kurzgesagt. Youtube. (2015). Quantum Computers Explained – Limits of Human Technology. [online video]. Access : https ://www.youtube.com/watch ?v=JhHMJCUmq28 >> Tzezana, R. Youtube. (2016). Could human civilization spread across the whole galaxy ? [online video]. Access : https ://www.youtube.com/watch ?v=Rpy9Qp7NAaw
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Iconography p. 24 Adapted from Grant, Pr. & Lack, D. (1986). Phylogeny of the Galápagos finches. [online]. Access : http://www.zo.utexas.edu/courses/bio301/chapters/Chapter7/ Chapter7.html p. 26 Dawkins, R. (1996). NetSpinner. [online]. Access : http://scilib-biology.narod.ru/ Dawkins/Mount/Climbing_Mount_Improbable.htm p. 29 Alan, R. (1952). The IAS Machine. [online]. Access : https://www.ias.edu/ideas/2012/george-dyson-ecp p. 29 Barricelli, N. (1953). Ones and Zeros. [online]. Access : http://nautil.us/issue/14/mutation/meet-the-father-of-digital-life p. 30 Galloway, A. (s.d.). Psychedelic Barricelli. [online]. Access : http://nautil.us/ issue/14/mutation/meet-the-father-of-digital-life p. 33 Palisade. (s.d.). Evolver. [online]. Access : https://www.palisade.com/evolver/ p. 35 NASA. (2008). Radio Antenna. [online]. Access : https://www.nasa.gov/centers/ ames/news/releases/2004/antenna/antenna.html p. 39 (s.d.). Game of Life. [online]. Access : https://erikonarheim.com/labs-page/ p. 41 Dawkins, R. (1986). Biomorphs. [online]. Access : https://c4rin3.wordpress.com/ tag/design-numerique/ p. 43 (2012). Al Biles. [online]. Access : https://www.youtube.com/watch?v=rFBhwQUZGxg p. 43 (s.d.). Al Biles Performing. [online]. Access : http://www.rochestercitynewspaper. com/rochester/critics-picks-frank-deblase/Content?oid=2625129 p. 45 Sims, K. (1994). Evolved Virtual Creatures. [online]. Access : http://www.tgdaily. com/web/134541-is-aggression-crucial-for-the-evolution-of-intelligence-and-isskynet-inevitable p. 45 Sims, K. (1994). Evolved Virtual Creatures. [online]. Access : http://folksonomy. co/?permalink=3722 p. 47 Pape, D. (1999). Galàpagos. [online]. Access : https://commons.wikimedia.org/ wiki/File:Karl_Sims_-_Galapagos_-_ICC.jpg p. 49 Draves, S. (s.d.). Electric Sheep. [online]. Access : http://scottdraves.com/sheep. html p. 51 McCormack, J. (2004). Eden. [online]. Access : http://jonmccormack.info/artworks/eden/ p. 53 Schmitz, M. (2004). Genotyp. [online]. Access : http://interaktivegestaltung.net/ genotyp/
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p. 55 Schmitz, M. (2006). Evolving Logo. [online]. Access : http://interaktivegestaltung.net/evolving-logo-2/ p. 57 McCormack, J. (2009). Colourfield. [online]. Access : http://jonmccormack. info/artworks/colourfield/ p. 59 Schmidt, K. (2016). Crystal Math. [online]. Access : http://www.creativeapplications.net/holo/holo-2-the-grand-tour/ p. 70 Adapted from Pitt Rivers, L. (1875). Evolving Artefacts. [online]. Access : http:// web.prm.ox.ac.uk/rpr/index.php/article-index/12-articles/631-whitchapel-1875. html p. 84 Adapted from Dawkins, R. (1996). Mount Improbable. [online]. Access : http:// scilib-biology.narod.ru/Dawkins/Mount/Climbing_Mount_Improbable.htm p. 89 Ofria, C. (1993). Avida. [online]. Access : https://fr.wikipedia.org/wiki/Avida_ (logiciel)
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Shaping Mount Improbable How should one start an EAD by computer ? I propose to begin with a description of the schema of a project in five parts (covering abstraction, transcription and translation) : 1) the solution space, 2) the major parameters, 3) the search space, 4) the design space and 5) design constraints. To help explain this proposed method, I will decompose the GA I used to produce the visual universe of this book (e.g. title). Thou shalt define the solution space By solution space, one must understand the explorable space in which the whole body of generated solutions or possible adaptations are found. This space will cover parts of perception such as sight, hearing, touch or a combination of those. It defines which perceptions the digital universe will communicate with. Of course, the generated solutions may not explore the total space depending on how the universe evolves and what constraints are encoded. The solution space answers the following questions : What is a possible solution to express my intent and solve the problem ? What could be created to meet the demanded requirements ? For the various organisms in this paper, I constrained the genetic algorithm to the perception of sight. Since the content was to be printed, I concentrated on a static output. The solution space is therefore in the static-visual realm. Thou shalt determine the major parameters As in the natural world, certain constants must be present for the calculation of all other variables. One must define the basic constants of the design. This could be the laws of physics such as a gravitational constant. Essentially, the major parameters dictate how the universe Shaping Mount Improbable | 121
behaves and what is possible. One lays these parameters by answering the following : What can the digital universe do ? And what can it not do ? What are the immovable objects and unstoppable forces ? The major parameters of the organisms here are : a two-dimensional surface of 256 by 256 pixels, a black to white colour spectrum, and a restricted maximum circle size. Thou shalt decide the search space The observable changes in an organism through the generations are the result of underlying variations. These variations occur in the search space : the genotype of the design. This comprises all the possible combinations of underlying variables in the desired format (four nucleotides for example). Here, one must transcribe the variables and algorithms of the phenotypic expressions into a common language which can then be shared, combined and mutated. The search space answers the following : What common language and structure are the organisms based on ? What does the genetic information look like ? For the sake of simplicity, the search space could resemble that of all natural things : DNA. All variables and algorithms would be the translation of nucleotide sequences, divided into genes and further divided into codons (nucleotide triplets). The search space for my GA relies on five characters (‘A’, ‘T’, ‘C’, ‘G’) grouped into genes. I chose this genotypic representation for its resemblance to DNA in nature. Each gene produces a floating point number when translated. Thou shalt frame the design space The design space comprises all the possible variations of the organism’s phenotypic traits. Effectively, one must describe the expressions of the organisms ; what they look like, and how they behave. This can then be retro-translated into numerical variables and algorithms. 122
The design space answers : What does the process of morphogenesis produce ? What are the possible visible traits of the organisms ? Through the simulation of the reaction diffusion algorithm, the various organisms produce their phenotype. Each gene from the search space will correspond to variables of the algorithm ; namely feed rate, kill rate, diffusion rate of chemical element A, diffusion rate of chemical element B, and the change in time. Thou shalt expound the design constraints By design constraints, one should understand the collection of fitness criteria and their respective weights or the simulation of an ecosystem with organisms of varying strengths and weaknesses. These will lead the evolution of the organisms in a certain direction. One should start with the extremes, the boundaries which the population should not exceed. Then, one must define various subtleties by making educated guesses and actually seeing the problems that need to be solved. Most often, design constraints will be added opportunistically over time when the designer considers it necessary. The problem of aesthetics is proven to be most difficult to solve with design constraints and it is best left to the judgement of the designer, embodying a beauty-detector. One should consider including a governing fitness criterion which can be manipulated by the designer or user to directly impact the fate of the evolving universe. Finally, the design constraints answer the following : What expressions of the organisms are preferred ? What would one like to observe in the evolving organisms ? As fitness rules : evolved circles that are fully black, fully white or a range of gray in between are considered unfit. Organisms whose simulations don’t exceed a certain area are removed. Those whose change from black to white pixels are too rapid or too slow are also viewed as unfit.
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Solution space
Application of design constraints
Design space
Application of major parameters
Search space
Once initialised in this manner, it becomes simpler to see the various difficulties. What remains now is to include the techniques of variation, selection and inheritance. Variation Variation allows for the diversity of a population to grow and avoids the apparition of similar organisms which would compromise the search. Oftentimes, random variations are deleterious to the organism, although they are sometimes benign or advantageous. I will —as many do— draw inspiration from nature to describe possible variations in the genotype. The mutation rate (a measure of probability of mutation over time) is a characteristic which can differ from population to population. One must keep in mind that a low mutation rate will result in low diversity and the solutions will tend towards local optima. A high mutation rate will result in high diversity (but the solutions may never stabilise). Therefore, as Barricelli noticed, one must keep the balance : “The trick was to tweak his manmade laws of nature—‘norms,’ as Barricelli called them—which governed the universe and its entities just so. He had to maintain these ecosystems on the brink of pandemonium and stasis. Too much chaos and his beasts would unravel into a disorganized shamble ; too little and they would homogenize. The sweet spot in the middle, however, sustained life-like processes.” (Hackett, 2014) Random mutations may happen at two levels : the nucleotide level and the gene level. At the nucleotide level, individual nucleotides may turn into different nucleotides through conversion (see conversion). They can also be removed through deletion (see deletion), or, be added through insertion (see insertion). At the gene level, whole sequences of nucleotides may be turned head Opposite A diagram showing the implementation of the five steps.
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over heels through inversion (see inversion), or, replace another sequence through substitution (see substitution). Gene transmission can stutter and be copied twice through duplication (see duplication), or, exchange positions with another gene through translocation (see translocation).
Opposite Two diagrams showing the various types of mutations possible.
Selection Fitness calculation If certain fitness criteria are clearly quantifiable, one can program them
Mating probability
into the system from the start. However, concerning the design goodness and aesthetics problem, I propose to attribute fitness scores in the following manner : the tool must integrate an adjusting fitness landscape. The designer, when either “liking” or “disliking” an organism will affect the evolution. Basically, the underlying values will be recorded as
Mating probability
Fitness
fit or unfit, creating peaks or valleys for those genotypic configurations. Mate selection Mate selection can then happen in multiple ways. It can be non-existent (see graph 1), analogically to wind pollination, where any sexually functioning organism may partake in mating. However, this
Mating probability
Fitness
is often combined with certain selection rules which weed out the lower fitness organisms. Parent selection may solely pick organisms above a certain fitness value (see graph 2), or, it may calculate an increasing probability with an increasing fitness (see graph 3). Mate selection can also be inexistent and replicate asexual reproduction. Inheritance “When an organism became maximally fit for an environment, the slightest variation would only weaken it. In such cases, it took at least two modifications, effected by a cross-fertilization, to give the numerical organism any chance of improvement. This indicated to Barricelli that symbioses, gene crossing, and ‘a primitive form of sexual reproduction,’ were 126
Fitness Above Three graphics showing possible mate selection variants. The vertical axis increases in fitness, and the horizontal in mating probability.
Nucleotide variations
AT G A A C C G C AT C TA G
DNA strand
AT G TA C C
Conversion
AT G – A C C
Deletion
AT G C A A C C
Insertion
Gene variations
Gene1 Gene2 Gene3 Gene4 Gene5 Gene6
DNA strand
Gene1 Gene2 2eneG Gene3
Inversion
Gene1 Genen Gene3
Substitution
Gene1 Gene2 Gene2 Gene3
Duplication
Gene3 Gene2 Gene1
Translocation
essential to the emergence of life. ‘Barricelli immediately figured out that random mutation wasn’t the important thing ; in his first experiment he figured out that the important thing was recombination and sex,’ Dyson says.” (Hackett, 2014) As Barricelli realised, the gene crossing of genes are crucial. Nonetheless, one question remains : which parents should mate ? Can any two genetic makeups be coupled ? This can be solved using genetic distance (genetic distance is relative to the resemblance of two genotypes). One must then choose what distance should be applied. Restricting the distance to the extreme would result in incestuous mating, whereas expanding too wide would mean inter-specie copulation (a small probability of this happening may be good for unforeseen side effects). It seems the least detrimental behaviour is to specify a range which does not look too close, nor too far. Once the partners are chosen, crossover happens. This exchange and recombination of underlying data results in the production of the offspring for the new generation. Luckily, this step is much easier in computers than in nature. The coalescence can be programmed in several ways. One parent can provide a certain amount of information, stop, and the partner provides the rest (see crossover 1). The amount inherited can be random or based on the parents’ fitness. The offspring can receive a random blend of the genetic information (see crossover 2). The blending ratio can be based on the fitness of each parent. The offspring receives a random blend controlled by the genes’ beginnings and ends (see crossover 3). The blending ratio may also be based on fitness. With asexual organisms, the complete genotype of one is inherited.
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Opposite Three possible crossover methods.
Crossover
AT G A A C C G C AT C TA G
Parent 1
AT G A C ATAT T C A C G G
Parent 2
AT G A A C TAT T C A C G G
Offspring Split point
AT G A A C TAT T C C TA G
Offspring Split points
Gene1 Gene2 Gene3 Gene4 Gene5 Gene6
Parent 1
Gene1 Gene2 Gene3 Gene4 Gene5 Gene6
Parent 2
Gene1 Gene2 Gene3 Gene4 Gene5 Gene6
Offspring Split points
Glossary A Abstraction The uncovering and definition of general rules from a series of specific examples. Ant colony optimisation A technique that uses probability to solve computational problems such as finding a good path through a graph. C Codon A sequence of three nucleotides. D Deoxyribonucleic acid (DNA) The molecule made up of nucleotides that carries all the genetic instructions used in the growth, development, functioning and reproduction of all living organisms. Designoids “Designoid” objects have an internal and external complexity that make one believe they have been exquisitely created for a specific purpose. Differential evolution A method that optimises a problem by iteratively trying to improve a candidate solution with regard to a given measure of quality. E Endosymbiosis Symbiosis of an organism with other organisms internal to it. Epigenetics The study of changes in organisms caused by modification of gene expression rather than alteration of the genetic code itself. Evolution strategy An optimisation technique based on ideas of adaptation and evolution.
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Evolutionary algorithm An evolutionary algorithm uses mechanisms inspired by biological evolution, such as variation, selection, and inheritance.
of neural units modelling the way a biological brain solves problems with large clusters of neurons connected by axons.
Evolvable hardware A field focusing on the use of evolutionary algorithms to create specialised electronics without the intervention of manual engineering.
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F Field-programmable gate array An integrated circuit designed to be configured by a customer or a designer after manufacturing —hence the name : “field-programmable”. G Gene A region of DNA made up of nucleotides delimited by a start and stop codon. It is the molecular unit of heredity in biology. Genetic algorithm A meta-heuristic method inspired by the process of natural selection that belongs to the larger class of evolutionary algorithms.
Particle swarm optimisation A computational method that optimises a problem by iteratively trying to improve a candidate solution with regard to a given measure of quality. Phenome The set of all phenotypes expressed by a cell, tissue, organ, organism, or species. Phenotype The composite of an organism’s observable characteristics or traits, such as its morphology, development, biochemical or physiological properties, behaviour, and products of behaviour (such as a bird’s nest). Primordial soup A solution rich in organic compounds in the primitive oceans of the earth, from which life is thought to have originated. S
Genetic programming A technique whereby computer programs are encoded as a set of genes that are then modified using an evolutionary algorithm.
Symbiosis A close and often long-term interaction between two different biological species.
Genome The genetic material of an organism, it consists of all its genes.
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Genotype The DNA sequence of the genetic makeup of a cell that determines a specific characteristic of that cell/organism/individual. N Neural networks A computational approach that is based on a large collection
Transcription Conversion of numerical variables (e.g. integers, floats) into a common language (e.g. bytes). Translation Externalisation of an idea in one’s mind into the perceptive realm.
Acknowledgements I would like to give special thanks to Daniel Pinkas, for his high quality tutoring, intelligent suggestions, and exceptional precision in word selection. Special thanks also go to Geraldine Donaldson for proofreading my paper and including the most hilarious comments which sadly do not appear in this final version. Moreover, I thank Esther & John Donaldson for proofreading and providing the best moral support parents could give. Many thanks go out to Coline Gavard for putting me back on the right track when I started to get lost. Similarly, I thank Camille Rattoni for her interesting suggestions and analyses. I also thank Romain Schaller & Isabelle Pierre for their important life lessons and for pushing me out of my comfort zone. Last but not least, I would like to thank my closest friends for all the small improvements they brought to this document and for the wonderful support on their part.
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Author, Conception, Design : Tutor: Proofreading : Printing : Binding :
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