Wu_Di_860315_AIR_PART_B by Di Wu

S T U D I O

A I R PART B

2018, SEMESTER 2, MOYSHIE ELIAS DI WU 860315

TABLE OF CONTENT PART B

CRITERIA DESIGN

B.1.

RESEARCH FIELD - GENETICS

B.2.

CASE STUDY 1.0

B.2A.

L-SYSTEMS & LOOPS

B.2B.

ANALYSIS - BLOOM PROJECT

B.2C.

COMPONENT DESIGN &MANUAL RECURSION

B.3.

CASE STUDY 2.0

B.4&5 TECHNIQUE: DEVELOPMENT & PROTOTYPES B.6.

TECHNIQUE: PROPOSAL

B.7.

LEARNING OBJECTIVES & OUTCOMES

B.8.

APPENDIX - ALGORITHMIC SKETCHES

BIBLIOGRAPHY

CRITERIA DESIGN .1 RESEARCH FIELD - GENETICS .2 CASE STUDY 1.0 .2A L-SYSTEMS & LOOPS .2B ANALYSIS - BLOOM PROJECT .2C COMPONENT DESIGN &MANUAL RECURSION .3 CASE STUDY 2.0 .4 & 5 TECHNIQUE: DEVELOPMENT & PROTOTYPES .6 TECHNIQUE: PROPOSAL .7 LEARNING OBJECTIVES & OUTCOMES .8 APPENDIX - ALGORITHMIC SKETCHES

.1 rESEARCH FIELD - GENETICS Genetic Algorithms VS Recursive Aggregation Recursion, in mathematics and computer science, is a method of defining functions in which the function being defined is applied within its own definition. It describes a process of repeating objects in a self-similar way. [1] It emphasises the loop of data and top-to-bottom recalling basic rules or definition. The complex form and the process of growth can atomize into the basic definition. These definitions are called iteratively. While it is just a simple process of non-response self-assembly. It is more like an assembly shop where similar components are assembled according to the instruction. As for the fabrication of components and the outcome, recursion will not make any choice and decision. Hence, recursive aggregation is a bottom-totop generative method and a process of mass production without selection and response. In contrast with recursive aggregation, a genetic algorithm emphasises more about breeding, selection and response to environments. Ad Frazier summarised that genetic algorithms are highly parallel, evolutionary, and adaptive. [2] Parallel means "Generates a population of points at each iteration. The best point in the population approaches an optimal solution." [3]

Evolution results from the variations achieved through gene crossover and mutation which take place during the iterative exchange and change of information. [4] "It is not the strongest of the species that survives, nor the most intelligent , but the one most responsive to change." --- Charles Darwin Selection is an important criteria and method to stimulate the evolution of genes. It weeds out the genes that cannot adapt to the environments and optimises the genes so that new generations can inherit "beneficial and survival-enhancing traits" from those selected parameter values. [5] Thus, compared with simple recursive aggregation, genetic algorithms enrich and optimize the population and quality of design outcomes and gene pool. Also, genetic algorithms enhance the ability of responding to fitness functions [6], which is more organic. However, recursive aggregation plays an important role for the iterations during the breeding process of genetic algorithms.

[1]. WeWantToLearn.net, Recursive Growth through Aggregation <https://wewanttolearn.wordpress.com/2014/11/13/recursive-growth-throughaggregation/> [Accessed on 10 September 2018] [2]. John Frazier, Evolutionary Architecture (London: Architectural Association, 1995), p. 58. [3]. The MathWorks, What Is the Genetic Algorithm? <https://www.mathworks.com/help/gads/what-is-the-genetic-algorithm.html> [Accessed on 10 September 2018] [4]. Branko Kolarevic, Architecture in the Digital Age: Design and Manufacturing (New York; London: Spon Press, 2003), p. 23. [5]. Kolarevic, Architecture in the Digital Age, p. 24. [6]. Philippe Marin, Jean-Claude Bignon, and HervĂŠ Lequay, "A Genetic Algorithm for use in Creative Design Processes". HAL, (2008). <https://halshs. archives-ouvertes.fr/halshs-00348546>. p. 2 [Accessed on 10 September 2018]

.1 Research field - genetics Recursive Aggregation - Primitives Aranda\Lasch, IPC, Thyssen-Bornemisza Art Contemporary, The Design School at ASU "Primitives" is a series of recursive aggregation designed by Aranda\Lasch, IPC, Thyssen-Bornemisza Art Contemporary, The Design School at ASU, and many artists and designers. Primitives consist of dispersed furniture elements that appear like rock piles, each one unique but formed from the same universal building block [7], which is the basic component of the aggregations. Basically, Primitives, the product of recursion, gives the impression of "[l]ike microcosms in the distance, the clusters are imagined as islands falling apart and building back up, organizing and eroding at once." [8] The designers also intended to express the logic and modularity of recursive aggregation and how the universe assembles itself through the growth of a single crystal. The process of the aggregation is simple recursion. The initial component is a octahedral unit. By analysing the characteristics of the basic unit (such as 8 faces and their unique features: 2 equilateral triangles and 6 isosceles

triangles), three different rules of fractal growth were set. Serpinski growth tends to develop more branches and take over more space, which contributes to the form generation. Subdivision fracts the component. All of these rules are processes of repeating objects in a self-similar way (move, orient, scale, atomize). By calling these rules over and over again and setting the times of iteration, the components were assembled and grow into an aggregation. The outcomes cannot be predicted, could be a mass, linear, sprial, irregular, or regular... Although the outcomes will experience artificial selection for particular functions, the algorithm was applied sequentially not in parallel. The generation cannot respond to the environments and also lacks interactions. It grows programmatically to self-assemble. However, the bottom-top design method creates more unexpected and unpredicted results. Hence, the benefit of recursion is rapid reproduction.

Fig. 1. Primitives recursive aggregation

[7]. Aranda\Lasch, Primitives, Design Miami <http://arandalasch.com/works/modern-primitives-in-miami/> [Accessed on 12 September 2018] [8]. Aranda\Lasch, Primitives, Venice Biennale <http://arandalasch.com/works/modern-primitives-venice/> [Accessed on 12 September 2018]

Fig. 2. The component and rulesets of Primitives

Fig. 3. The outcome of Primitives

.1 Research field - genetics Genetic Architecture - Rlues of Six Aranda\Lasch, and Matthew L. Scullin Rules of Six is a project commissioned by the Design and Elastic Mind exhibition at the Museum of Modern Art in New York curated by Paola Antonelli. [9] They intended to explore the self-assembly of components and apply bottom up rules of formation. The most important aspect is that the designers tried to grow an aggregation form through simple interactions between components or molecules. [10]

simulate the process of metabolism.

The growth and evolution of the hexagons follow the rules of recursive aggregation. The initial component generates a new generation and pass the genes to the next generation. Something new is that components are able to interact with their neighbourhoods. According to the conditional rules set in prior, once the new generation meet the conditional rules, some previous generations may decline. a custom piece of software was written in the Processing programming environment that simulates formation over time in the same way molecules assemble themselves. [11] Through this kind of mechanism, genetic algorithms realise genetic optimization, evolution and

Thus, from this project, it is obvious that genetic algorithms' capability to create massive elements by crossover genetic rules. Then each generation will experience the environments' selection. The survival kept the optimized parameter value and accumulated these genes through a variety of recursion in order to obtain a pattern that have the best performance.

The algorithm of this project met the criteria for success of genetic architecture: the genetic information, the rules of hexagon, was recurred and passed accurately. [12] In the simulation of selective environments, different generations compete. The variation and mutation were selected by the rulesets.

Through this project, we can the possibility of genetic algorithms that generate the populations of outcomes and the variations of possibilities. And it is much more organic than the simple recursive aggregation.

[9]. Aranda\Lasch, Rules of Six <http://arandalasch.com/works/rules-of-six/> [Accessed on 13 September 2018] [10]. Aranda\Lasch, Rules of Six. [11]. Aranda\Lasch, Rules of Six. [12]. Frazier, Evolutionary Architecture. p. 99.

Fig. 4. The growth and interactions of Rules of Six

Fig. 5. Rules of Six was apllied in three-dimensional wall relief

.2 Case Study 1.0 .2A .2B .2C

L-SYSTEMS & LOOPS ANALYSIS - BLOOM PROJECT COMPONENT DESIGN &MANUAL RECURSION

.2a case study 1.0 L-SYSTEMS AND LOOPS Aristid Lindenmayer L-system is the abbreviation of Lindenmayer systems, which is a mathematical theory of plant development. The core of L-system is parallel string rewriting system. Aristid Lindenmayer, a biologist, introduced this new type of rewriting system in 1968. [1]

Pre-L-system Before L-system, the definition of rewriting system had beed studied. "Rewriting is a technique for defining complex objects by successively replacing parts of a simple initial object using a set of rewriting rules." [2] The essential difference between Chomsky grammars and L-systems lies in the method of applying productions. In Chomsky grammars productions are applied sequentially, whereas in L-systems they are applied in parallel and simultaneously replace all letters in a given word. This difference reflects the biological motivation of L-systems. The core is parallel, which means variable divisions may generate at the same time. [3] In order to model higher plants, Frijters and Lindenmayer, and Hogeweg and Hesper added the geometric aspects, such as the lengths of line segments and the angle values in a post-processing phase. [4]

Based on a recursive, rule-based branching system, L-system use string rewriting rules to successively replace previous generation with new generations. “A string rewriting system consists of an initial string, called the seed or Axiom, and a set of rules for specifying how the symbols in a string are rewritten as (replaced by) strings.” [5]

Simple L-system “Axiom: A rules: Rule #1: A = AB Rule #2: B = BA n=0: A n=1: AB (A becomes AB according to Rule #1) n=2: ABBA (A becomes AB according to Rule #1, while B becomes BA according to Rule #2. In result we get ABBA) n=3: ABBABAAB n=4: ABBABAABBAABABBA … “ [6] The L-System starts with the axiom ‘A’ and iteratively use the rules to replace previous strings. On each iteration a new string/word is derived. [7]

Theory

[1]. Unknown, “Chapter 1 Graphical modeling using L-systems” <http://algorithmicbotany.org/papers/abop/abop-ch1.pdf> [Accessed on 6 September 2018]. (p. 2) [2]. Unknown, “Chapter 1 Graphical modeling using L-systems”. (p. 1). [3]. Frazier, Evolutionary Architecture. p. 58. [4]. Unknown, “Chapter 1 Graphical modeling using L-systems”. (p. 6). [5]. Morphocode, GETTING STARTED WITH RABBIT: Intro to L-systems <https://morphocode.com/intro-to-l-systems/> [Accessed on 6 September 2018] [6]. Morphocode. [7]. Morphocode.

Fig. 1. Plant growth modelling using Processing and Lindenmayer Systems.

.2A L-SYSTEMS & LOOPS Spin Needles I cultivated a species with needle-shaped wings and in the spiral layout. Test more iterations to find out which genes control the needle-shaped characteristic. Keep this data structure or modify slightly. And change other genes to generate a family.

Iteration = 8 Axiom = BED A= B = CAB C = BAC D = CAE E = BDC

Iteration = 8 Axiom = BED A=B B = CAB C = BAC D = CAE E = BDC

Iteration = 9 Axiom = BED A=B B=C C = BAC D = CDE E = BDC

Iteration = 8 Axiom = BED A = CB B = BC C = ABC D = DE E = BDC

Iteration = 8 Axiom = BDC A = CB B = BC C = BAC D = DE E = BDC

Iteration = 7 Axiom = BED A = CAB B = BAC C = CAE D = BDC E=A

c d

b A

Iteration = 8 Axiom = BED A = BC B=C C = BAC D = CDE E = BDC

Iteration = 8 Axiom = BDC A = CB B = BC C = BAC D = DC E=

Iteration = 7 Axiom = BDE A = CAB B = BAC C = CE D = BDC E = BC

Iteration = 6 Axiom = ABCD A = CAB B = BAC C = CAE D = BDC E = BCD

.2A L-SYSTEMS & LOOPS Bloom & Petal Through lots of iterations, I found repeating certain branch will form beautiful curvature. The branch will roll up. Add other branches which are close to this type of branch will form nice petals

Iteration = 10 Axiom = ADE A = CA B = CD C = CD D=D E=A

Iteration = 8 Axiom = ADE A = CA B = CD C = CB D = CD E=A

Iteration = 9 Axiom = CDE A = CA B = CD C = CB D=D E=A

Iteration = 10 Axiom = ADE A = CA B = CD C = CD D = CD E=A

Iteration = 10 Axiom = CDE A = CAD B = CD C = CD D = CD E=A

Iteration = 10 Axiom = BE A = CA B = BC C = CD D = DC E=A

Iteration = 10 Axiom = ADE A=C B=B C = CBD D=C E = CDA

Iteration = 16 Axiom = AD A = CAD B = CD C = CD D=D E=A

Iteration = 10 Axiom = AE A = BAC B = BD C = BD D=B E=B

Iteration = 12 Axiom = BCDE A = ACD B = CD C = CD D=C E=A

.2A L-SYSTEMS & LOOPS Fan-cy Branch E is in the opposite direction. I took advantage of E to create divergence from early generation.

Iteration = 4 Axiom = ABCDE A = BCD B = BCD C = BCD D = BCD E = BCD

Iteration = 9 Axiom = ADE A = CD B = CD C = CD D = CD E = CD

Iteration = 6 Axiom = ADE A = BC B = BC C = CD D = BC E = CB

Iteration = 9 Axiom = ADE A = CB B = CB C = CB D = CB E = CB

Iteration = 6 Axiom = ADE A = BCE B = BCE C = CD D = BC E = CB

Iteration = 8 Axiom = CDE A=D B = CB C = CB D = CD E=D

Iteration = 9 Axiom = ABE A = BC B = CD C = CD D = BC E = BC

Iteration = 8 Axiom = ADE A = CB B = CB C = CB D = CD E = CE

Iteration = 8 Axiom = ABE A = ABC B = CD C = CD D = BC E = CB

Iteration = 9 Axiom = ADE A=B B = CB C = CB D = CD E=D

.2A L-SYSTEMS & LOOPS Blast Branch E is in the opposite direction. I took advantage of E to create some disorders. Like forming a turbullence at the center. Then the turbulence disperses in all directions.

Iteration = 4 Axiom = ABCDE A = BCE B = BCE C = BCE D = BCE E = BCE

Iteration = 7 Axiom = AD A = CD B = CDE C = CDE D = CDE E = CDE

Iteration = 10 Axiom = BCD A = CA B=D C = DE D = BC E=A

Iteration = 9 Axiom = BCD A = CA B = CD C = DE D = BC E = AC

Iteration = 7 Axiom = CD A = CA B=D C = DE D = BC E=A

Iteration = 7 Axiom = AE A = CA B=D C = DE D = BC E =A

Iteration = 8 Axiom = ADE A = CAD B = CD C = CD D = BE E=C

Iteration = 7 Axiom = ACDE A = CBE B = CDE C = CDE D = BEC E=C

Iteration = 10 Axiom = ADE A = CE B = CD C = CD D = BE E=C

Iteration = 8 Axiom = ACE A = CB B = CD C = CD D = BCE E = AC

.2A L-SYSTEMS & LOOPS Claw

Iteration = 7 Axiom = ABE A = CE B = CB C = BCE D = BCE E = CB

Iteration = 7 Axiom = ABD A = CE B = CB C = BCE D = BCE E = CB

Iteration = 7 Axiom = BCD A = CE B = CB C = BCE D = BCE E = CB

Iteration = 7 Axiom = ABD A = ACE B = CB C = BCE D = BCE E = CB

Iteration = 7 Axiom = ABD A = ACE B = CB C = BDE D = BDE E = CB

Iteration = 7 Axiom = ABD A = CE B = CB C = CDE D = CDE E = CB

Iteration = 7 Axiom = ACDE A = CE B = CB C = BCE D = BCE E = CB

Iteration = 7 Axiom = ABD A = CE B = CB C = BCE D = BCE E = CBD

Iteration = 7 Axiom = ABD A = CE B = BCE C = BCE D = BCD E = BCD

Iteration = 5 Axiom = ABC A = CDE B = ACD C = ABD D = ACE E = BCD

.2b - Bloom Project Bloom is an example that combined genetics and recursive aggregation to generate a form. By the cells carefully designed by designers, the public is guided and inspired to set their own favourite rules to assemble

pieces of "Bloom" for entertainment. Meanwhile, the public unwittingly create an aggregtion by the artificial selection to the mutation of the morphogenesis of the genetics.

.2b ANALYSIS - bloom PROJECT RECURSIVE AGGREGATION / GENETIC ARCHITECTURE Alisa Andrasek / Jose Sanchez Overview:

goal and purpose (environmental response). [6]

Bloom was designed to celebrate the 2012 London Olympic Games. It was commissioned by the City of London and designed by Alisa Andrasek / Jose Sanchez. [1] The gene is designed by the designers and the growth and the variations are implemented by the public. Unpredictable forms are designed, altered and dismantled by the crowd. [2]

The growth is a recursive aggregation operated by the public. During the process, different users' creativity of using the genes/intrinsic rules set their rules and their preference as a kind of artificial selection that determines the final expressions. For instance, the designers intended to create a bench for a functional and aesthetic purpose, Unlike the assembly of other "blooming" order, the designers only use parametric linear arrays arranged the modular elements. [7] Besides, people are able to plug one slot into any slot of three slots to form different angles and sequences, l such as spirals, linear order and so on. Those pre-set modes store the beneficial and survival genes (parametric values) to teach people to create ideal forms quickly.

The relationships with genetics and recursive aggregation: Its design of the cellular pieces is the process of encoding the genes and setting internal rules which determine the environmental response (i.e. set the rules how people could assemble these pieces). [3] On the one hand, the variations are mainly determined by the gene crossover and mutation. [4] In this project, based on the principles of connections of vectors, "Bloom" piece was designed three possible connection points in an asymmetric plate, which allows the generation of a variety of spiral connections. [5] Also, some pre-seeded behaviours were encoded in the cells in order to teach /guide different participators how to reach a specific

Thus, during the process of designing a component, the parameters of primary sockets and secondary sockets have some connections with the expression of outcomes. However, what is interesting is that the recursive aggregation is more controlled by the users, which increases the possibilities of mutations. New emergence may generate.

Fig. 1. The aggregation of Bloom project

[1]. PLETHORA PROJECT, Winner WONDER SERIES Competition 2012 <https://www.plethora-project.com/bloom/> [Accessed on 29 August 2018] [2]. PLETHORA PROJECT. [3]. Branko Kolarevic, Architecture in the Digital Age: Design and Manufacturing (New York; London: Spon Press, 2003), p. 24. [4]. Kolarevic, Architecture in the Digital Age, p.23. [5]. The Bartlett School of Architecture UCL, Bloom by Alisa Andrasek and JosĂŠ Sanchez <https://issuu.com/bartlettarchucl/docs/andrasek_01_bloom_s05_update> [Accessed on 30 August 2018], p. 21. [6]. John Frazier, Evolutionary Architecture (London: Architectural Association, 1995), p. 75. [7]. The Bartlett School of Architecture UCL.

Fig. 2. Bloom aggregation

Fig. 3. A component of Bloom

Fig. 4. Bloom Aggregation

.2c Component Design & Manual Recursion

.2c component design & aggregation Designing nodes and skeletons will affect the trends of aggregations. The 2nd step is to fill muscle and skin.

Forky

Reef

Dart

Bony

Rigid

Wing 29

.2c component design & aggregation Bony

Bony is more like a skeleton. An assasssin tore his disguise and dove for his taget. Have crept for a long time, he should show off his crazy to his enemies.

RULESET AXIOM = C A = BC B = AC C = ABC

If A intersects any component, keep A If B intersects any component except A, keep new B If C intersects any component, delete C

3RD GENERATION 2ND GENERATION 1ST GENERATION AXIOM

Manual aggregation

Top view

Right view

.2c component design & aggregation Reef

Reef is the home of marine creatures. The vault and dome formed by the components and the golden sunshine penetrating the water build a warm atmosphere. The reef embraces these lovely creatures.

RULESET AXIOM = C A = CD B = AC C = BCD D = BD

If B intersects with any component, delete B If C intersecss with any component, keep C If D intersects with any component, keep D

3RD GENERATION 2ND GENERATION 1ST GENERATION AXIOM

Manual aggregation

Top view

Right view

.2c component design & aggregation Wing

Don't be close to me! This is my cave! my territory! But those are not spikes. They are wings, the wings of dreams. The dreams are disillusioned. The feather became spiny. They are protecting a broken heart.

RULESET AXIOM = AB

If A intersect any component, keep A If B intersect any component except A, keep B

A = BD B = CD C = ABC D = BC

3RD GENERATION 2ND GENERATION 1ST GENERATION AXIOM

Manual aggregation

Top view

Right view

.2c component design & aggregation Rigid

Soldiers! Soldiers! Line up in defence formation! Our Lances, our swords, our daggers should be towards our enemies! Order and justice will defeat our enemies!

RULESET AXIOM = AB A = BCD B = BC C = AB D = AC

3RD GENERATION 2ND GENERATION 1ST GENERATION AXIOM

Top view

Right view

.3 CASE STUDY 2.0

.3 Case Study 2.0 AGGY-ATTACK COMPONENT AGGREGATIOIN Keys 1. Refer to the initial branch and the the relationships between parent and child branches as an instruction (ruleset) to grow next generations. The rule of the growth of each generation sould be go back to the initial genetic rule setting.

2. Recursion/ A loop as an engine/ operator to reference and manipulate the genes/ rules to grow the aggregation persistently.

Gene Coding 01

> Input L-shaped polylins for standardise the lengths and the directions of components in the initial parent component's coordinate. Blue one reperesents parent branch.

Set init set on establis

> 3. Set conditional rules, including internal conditional rules and external conditional rules to eliminate malformation and response to the environments:

2) External conditional rules: Environments have strong influence on the aggregation forms such that something obstructs the growth of aggregation. Thus, we can cultivate ideal aggregation forms by setting conditional rules to force the growth towards what we expected.

Set st generat the end The rul rules s

1) Internal conditional rules: genetically control the aggregation forms which is aimed to avoid the errors and "malformation" though the recursion is correctly run. e.g. the intersection and collision between to components.

Read the data of previous genration curves, current iteration, and select growth branches based on length heuristic. Grow next generation.

> Refer to initial plane and input geometry orient them, and use Mesh Mesh Intersection to test whether new generation intersect previous generation Cull index and Stream Filter get rid of the collison data.

Anslyse the sur compone letters to the

tial parent branch and the branches the parent in grasshopper in order to sh dummy branches.

> Redraw the second segment to create a heuristic handle as a guide handle. Establish a plane which is perpendicular to the first segment at the end of first segment. The next generation can be oriented relative to parent plane.

L-shaped polylines are exploded to two segments. The longer segment (1st) is used to control length standardisation. The shorter one (2nd) as a reference is used to create a coordinate of child branches.

tarting and Axiom. Each generation will te a new plane/new coordinate system at d of dummy branches for next generatiion. le of reference will go back to the genetic set at Step 4.

Orient the parent component based on the orientations of child branches in parent coordinate/ axis-systems. Modify dummy branches to control dummy components. The dummy components should intersect with parent component.

Reference the component as a brep. Modify the axes of the component.

10A

e the relationship between aggregation and rrounding environments. Exhaust - place ent mesh based on the plane and make correspond to final branches according index.

Use obstacle clasher, Mesh Mesh Intersection identify the intersections of components, and remove all null and invalid value form data tree. And culled and filtered data tree was output to realize the decline of branches when they touch obstacles.

Continue the recursion !

.4 & 5 TECHNIQUE: DEVELOPMENT & Prototypes

.4 & 5 TECHNIQUE DEVELOPMENT

COMPONENT 1

CNC Milling: make the mould with smooth surface and without sockets.

Undoubtedly, use 3D printing to make the whole component as prototype is much easier. But the sharp edges (like the curved edge) may be blurred and dull. Also, it is not economic for mass production.

CNC Milling: drill a hole for material injection. The hole in this direction is aimed to make liquid material distribute in the mould uniformly. Also, the handle can be removed easily. The surface can be sanded. It won't affect the appearance. Pink represent sockets. 3D printing: print the sockets and insert them into the mould. They should be stuck with the mould to avoid the movement of sockets when plastic material is injected and ensure they can be taken down easily when open the mould.

COMPONENT 2

The hole for injection CNC Milling: make the mould with smooth surface and without sockets. The directions and depth of sockets make sockets overlay. For precision, six-axis robotic arm is controlled by algorithm and used to drill the sockets.

3D printing is still the easiest and quickest way to digital fabricate the prototypes, because of many curved surfaces. For mass production, using moulds is more economic and using robotic arm is more accurate to drill complicated sockets.

COMPONENT 1 RULESET #1

C B A D

I am a rhino. Thick skin wraps around my body. Notice! the tiny hooks and spikes on my skin are my invisible weapon. Caution!. You cannot defeat me!. I will always stand and form a dome to protect my children underneath me!

RULESET AXIOM = BD A = BC B = AB C = BCD D = BC

3RD GENERATION 2ND GENERATION 1ST GENERATION AXIOM

COMPONENT 1 RULESET #2

C B A D

Come on! Come into my hug quickly. "I" have the most soft skin. You will be intoxicated with my sweet smell. Give me your hand and touch my tentacles. "I" promise you will forget everything... ha...ha... ha............

RULESET AXIOM = D A = BCD B = BC C = BC D = CD

3RD GENERATION 2ND GENERATION 1ST GENERATION AXIOM

COMPONENT 2 RULESET #1

A B

That is my treasures. Hahaha. "I" hide my claws under the thick fur. No one could notic the danger beneath the fluffy and lovely fur. "I" will grasp everything I desire.

RULESET AXIOM = ABCD A = AB B = CD C = AB D = BC

3RD GENERATION 2ND GENERATION 1ST GENERATION AXIOM

COMPONENT 2 RULESET #2

A B

Si--si----si-si-! "I" am hungry. I haven't eaten for a long time. Si--si--- Snake is hungry. Snake feel annoyed. Look at my serpentine body. Si-si-- I'm ready!

RULESET AXIOM = BCD A = BD B = ACD C = CD D=B

3RD GENERATION 2ND GENERATION 1ST GENERATION AXIOM

.6 Technique: Proposal The site that was chosen for aggy attack in MSD (Melbourne School of Design) is the hanging studio in the atrium. The atrium is filled with natural lighting and it is a large open space for students to study and discuss. The open and broad horizon in the atrium is beneficial for students to think, relax and imagine. That is why I saw the hanging studio every time, I always have a passion to climb the hanging studio. Extremely impulsive! Imagine I myself was a spider climbing on the facade of the hanging studio and saw the world from a different perspective. On the other hand, a rapidly increasing number of students in MSD have

made the atrium crowded. It became really hard to find a seat. Hence, I think of add vertical equipment instead of adding more horizontal levels, terrains or floors. Students can have fun vertically and the spatial development could be vertical. The form of the aggy-attack aggregation could be crazy and aggressive, like the alien invades the atrium and try to attack hanging studio. The exotic invaders occupy ferociously. They attack and occupy the studio. Because I used the component and aggregation of Part B2. For further inprovement, secondary elemnets will be added and Local differentiation will be applied at the next stage.

.6 TECHNIQUE PROPOSAL INVADE HANGING STUDIO

.1 Research field - genetics Recursive Aggregation DILLER SCOFIDIO + IRENFR Swiss Expo 2002, Yverdon-les-Bains, Switzerland

We are from... We don from. What we only k conquer. We assimilate our members. That is o are bony phagotroph... atrium of MSD, we expa aggressive.

No survival! Only bony must be conquered. Th "architectural student" aggressive we are. We i holes in the floating stu on the facade. We can s fear of the floating st in the studios.

We creep on the facade. of MSD. We hear the reve They are nearly crazy climbing recklessly and the hanging studio.

Carnival! Bonys! This is glance through the glass those hide in the studio us soon...

n't know where we are know is - invasion and any live creatures into our way of growth. We Engulfed beings in the anded and became more

ys! The floating studio he more beings called are devoured, the more insert our bony into the udio. The bony wave rush smell the shrinkage and tudio and the creatures

. We are the dominators elry of our new members. and insane. They are occupy everywhere on

s our world! Scornfully s of the hanging studio, os, Hahaha, you will join

.7 Learning Objectives & Outcomes I gradually understand the distinction between recursive aggregation and genetic architecture. The research let me see the power of recursion in terms of the generation of complex aggregation forms and reproduction. And the research about genetic algorithms reveals the strong connection to biology. The bottom-up design method shows me a new world in which I became confident and curious to learn the mechanism about how the genes/basic components and inner rules affect the morphogenesis, try to understand the variations caused by gene crossover and mutation. Through genetic algorithms, heaps of iterations enrich the gene pool and optimize the genes collaborating with selection in the environments. Based on the theory and research about L-systems and the practice, the intensive exercise and various iterations pushed my understanding further about how the inner and rules of genes are passed and optimized the performances. I played with rulesets and found some rules which may affect the aggregation forms. After the analysis of Bloom project, I accumulated some experience and get some inspiration from Bloom. And try to apply

what I got to component design, such as the node design may have an influence on the tendency of the final aggregations. Meanwhile, I preliminarily understood how natural selection and artificial selection work. Selection plays an important role in the evolution of aggregation. Although the outcomes of recursive aggregation and genetic algorithms are massive, complicated and unpredictable, they should become a necessary trend to find more possibilities and solution and they are more adaptive methods to adapt to this changeable world in the future. Techniques: I attempted to deconstruct the grasshopper algorithms of aggy-attack aggregation definition and summarized the result of deconstruction in part B3. By watching online tutorial video, I mastered the grasshopper algorithms for local differentation provided by my tutor. What's more, I combined two algorithms and introduce some new algorithms to design secondary components. I have learned and tried several types of visual communication to present my proposals, such as V-ray render, diagrams.

.8 Appendix - Algorithmic Sketches

B I B L I O G R A P H Y 1. Aranda\Lasch, Primitives, Design Miami <http://arandalasch.com/works/modern-primitives-in-miami/> [Accessed on 12 September 2018] 2. Aranda\Lasch, Primitives, Venice Biennale <http://arandalasch.com/works/modern-primitives-venice/> [Accessed on 12 September 2018] 3. Aranda\Lasch, Rules of Six <http://arandalasch.com/works/rules-of-six/> [Accessed on 13 September 2018] 4. Frazier, John, Evolutionary Architecture (London: Architectural Association, 1995) 5. Kolarevic, Branko, Architecture in the Digital Age: Design and Manufacturing (New York; London: Spon Press, 2003), pp. 3-62. 6. Marin, Philippe., Bignon, Jean-Claude., and Lequay, Hervé. "A Genetic Algorithm for use in Creative Design Processes". HAL, (2008). <https://halshs.archives-ouvertes.fr/halshs-00348546> [Accessed on 10 September 2018] 7 Morphocode, GETTING STARTED WITH RABBIT: Intro to L-systems <https://morphocode.com/intro-to-lsystems/> [Accessed on 6 September 2018] 8. PLETHORA PROJECT, Winner WONDER SERIES Competition 2012 <https://www.plethora-project.com/bloom/> [Accessed on 29 August 2018] 9. The Bartlett School of Architecture UCL, Bloom by Alisa Andrasek and José Sanchez <https://issuu.com/ bartlettarchucl/docs/andrasek_01_bloom_s05_update> [Accessed on 30 August 2018] 10. The MathWorks, What Is the Genetic Algorithm? <https://www.mathworks.com/help/gads/what-is-thegenetic-algorithm.html> [Accessed on 10 September 2018] 11. Unknown, “Chapter 1 Graphical modeling using L-systems” <http://algorithmicbotany.org/papers/abop/ abop-ch1.pdf> [Accessed on 6 September 2018] 12. WeWantToLearn.net, Recursive Growth through Aggregation <https://wewanttolearn.wordpress. com/2014/11/13/recursive-growth-through-aggregation/> [Accessed on 10 September 2018]

LIST

IMAGE

B.1.

Cover page. Marin, Philippe., Bignon, Jean-Claude., and Lequay, Hervé. "A Genetic Algorithm for use in Creative Design Processes". HAL, (2008). <https://halshs.archives-ouvertes.fr/halshs-00348546> [Accessed on 10 September 2018] Fig. 1 - 3. Aranda\Lasch, “Primitives, Venice Biennale” <http://arandalasch.com/works/modern-primitivesvenice/> [Accessed on 12 September 2018] Fig. 4-5. Aranda\Lasch, Rules of Six <http://arandalasch.com/works/rules-of-six/> [Accessed on 13 September 2018]

B.2A.

Fig. 1. Jones, Daniel John, "Plant growth modelling using Processing and Lindenmayer Systems." <http:// www.erase.net/projects/l-systems/> [Accessed on 6 September 2018]

B.3.

Cover page. PLETHORA PROJECT, “Winner WONDER SERIES Competition 2012” <https://www.plethora-project.com/bloom/> [Accessed on 29 August 2018] Fig. 1, 2, 4. PLETHORA PROJECT, “Winner WONDER SERIES Competition 2012” <https://www.plethora-project. com/bloom/> [Accessed on 29 August 2018] Fig. 3. The Bartlett School of Architecture UCL, Bloom by Alisa Andrasek and José Sanchez <https://issuu. com/bartlettarchucl/docs/andrasek_01_bloom_s05_update> [Accessed on 30 August 2018]

S T U D I O

A I R 2018, SEMESTER 2, MOYSHIE ELIAS DI WU 860315