AA EMTECH |EMERGENCE AND EVOLUTIONARY COMPUTATION by Berin Kocabaş

Emergent Technologies and Design / AA School of Architecture

ABSTRACT The ‘Emergence and Evolutionary Computation’ seminar, aims to understand the concept of multi-objective (fitness criteria) optimization process while applying the abstract principles of the evolution to a design approach for the development of towers. ‘Optimization’ stands as a critical research factor during this process, and it targets to find the fittest/ best solution to a given design problem. In this iterative process, the algorithm is applied to produce offsprings that breed from individuals with superior traits that are evaluated based on the fitness criteria. As more generations are produced during the optimization process, the chance for the superior genes to appear, increases, and the individuals tend to suit the environment better. The research for the design problem is conducted in three particular sequences that focus on a different scale and complexity levels to generate design solutions. In the first sequence, the experiment setup is prepared for the primitive tower geometry to run a multi-objective algorithm based on the selected fitness criteria (which are the geometric principles to enhance the spatial and architectural quality of the tower). The sequence two is designed for individual experiments that focus on integrating evolutionary developmental principles within the algorithmic setup. The IFC Tower in Guangzhou is used as a primitive tower and analyzed morphologically and environmentally to evolve variations in the new generations. The final sequence is a group experiment applied on a larger scale without any given limitations of size, generation count, or environmental context. The algorithm is driven with multiple fitness criteria that address the environmental context as well as the tower morphology. Emerging patterns are analyzed, and the design solutions are selected based on the comparison with the original primitive phenotype.

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TABLE OF CONTENTS INTRODUCTION.................................................................................................................................................7 SEQUENCE 1......................................................................................................................................................8 SEQUENCE 2...................................................................................................................................................26

EXPERIMENT 01............................................................................................................................28 EXPERIMENT 02...........................................................................................................................46

SEQUENCE 3...................................................................................................................................................66

CONCLUSION...................................................................................................................................................88

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INTRODUCTION In recent years, the evolutionary algorithms (either with single or multiple objectives) are commonly used in the fields ranging from architecture to music and economics. The evolutionary algorithms that held through search and optimization processes are derived from evolutionary principles studied by Darwin (1859) and Alfred Russel Wallace in the mid-1800s. After the notion of ‘natural selection’ is introduced to biology, it derived the cognition of ‘evolution’, that generated later the definition of ‘computational evolutionary algorithms’ in 1958 by Fraser. 1 Multi-objective algorithms ensure a better understanding of a design problem that has no obvious single solution due to multiple conflicting objectives that it refers to. It is a useful tool for minimizing user bias as well since the selection is made out of all the possible outcomes within a given set of rules. However, it still has initial control by the user as those rules, and the experiment setup that leads the whole process are created manually. In the research, the algorithms are used to generate solutions based on a given phenotype and the conditions. For this process to be called a successful experiment, the range of the results should show both geometric diversity and convergence towards the best fitness criteria. At Sequence 1, where the selected primitive geometry is optimized to generate a variety of tower phenotypes, the computational control on the algorithm is analyzed and understood. With the inferences of the first stage, both group members set up their experiments by enhancing the complexity of the algorithms. In Sequence 3, all the previous implications are converged, and the environmental context is also embodied in the algorithm. The leading research at the last stage was to experiment on the environmental context of the city Guangzhou in China, where the IFC Tower is located in a commercial district. Recently, the urban planning of Guangzhou started to become the focus of interest by the local people. ‘Guangzhou Urban Planning Exhibition Centre—the city ID of a brand new digital era—is now open to the public. The center is situated at the heart of the Baiyun New Town by the Baiyun Hill2. The Urban Planning Exhibition Centre aimed to show people how the city tissue is changing and highlight the traffic and modernization of the city with its setbacks. Sequence 3 of the experiment focused on urban planning as well and specified to connect the commercial and residential facilities while integrating the greenery and providing pedestrian-friendly network, which is now lacking in Guangzhou.

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In order to abstract the geometric and architectural parameters from the IFC Tower, it is analyzed in the plan and section views. The most specific architectural features of the towers are the core system, which appears to be a single-core until the 70th floor and continues as a multi-core system. From the 70th to 103rd floor, the multi-core system allows an atrium to emerge in the center of the 3 cores. Besides, there are regular mechanical equipment floors in every 20 floors that divide the streamline facade into 5 horizontal segments.

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After the analysis of the IFC Tower, the extracted parameters are selected to be the varying single and multi-core vertical circulation system and the division of the primitive geometry into 5 horizontal segments.

For the experiment, the primitive body plan is constructed with 5 horizontally divided body parts. The core system in the body plan is a continuous vertical circulation that remains as single-core until the horizontal body part 4 and continues as multi-core.

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The floor plans of the IFC Tower consists of 2 types, which the first one is a solid floor that a single core passes through the middle, and the second type has triangular atrium opening on the middle that is surrounding by 3 cores at the corners. As these 2 types of floor plans are taken as a basis for the experiment setup, variations of possible multi-core systems is explored further. The diagram below â&#x20AC;&#x2DC;Possible floor plansâ&#x20AC;&#x2122; portraying the possible floor plan layouts with varying vertice number of polygons (3-6). As the core geometry of the building changes from triangular to hexagon, this also affects the geometry of the phenotype and the facade, as also seen in the diagram.

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Besides the 5 horizontal segments of the primitive body plan, all the other architectural elements of the tower phenotype are exposed on the diagram above. The body plan of the tower consists of vertical circulations (ranging from 3 to 6 circulation cores), a single core that covers the multi-cores until the atrium space, office cores (solid floor plans only cut through by the vertical circulation), hotel floors with a hollow floor plan, solid shading elements on the facade and the terraces.

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5 fitness objectives are established in the experiment, based on the pre-defined design considerations. The first fitness objective is maximum atrium volume (which is located at the 5th segment of the tower) on the hotel function of the tower. The atrium volume desired to be maximixed in order to offer enhanced spacial quality for the hotel guests while allowing sky view through the atrium floor, from the lobby of the hotel. Following fitness criteria is determined to be the maximum straightness of the vertical cores to make it feasible for the elevator and stair installations.

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Fitness criteria 4 is the maximum terrace area on the south facade to perform as shading for the direct sunlight from the south. The diagram above displays the Pseudo Code for the terrace generation, in which the 2 surfaces (one is the tower geometry) are intersected to extract the intersection surface surrounding the tower geometry as a shading element. The surfaces on the tower excluding the shading elements are divided into contours representing the floors, and they are exposed outwards to generate the terraces. (So, on the tower facade, it is covered either with the solid shading element or the terraces, there are no empty surfaces). When the terraces are splitted from the middle and the southward geometries are selected, their area is evaluated as a fitness criteria that meant to be maximized.

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GENES

The diagram above illustrates the effecting genes on the 5 Fitness Criteria. The fifth gene, which is the â&#x20AC;&#x2DC;Height of each 5 Body Partsâ&#x20AC;&#x2122; has a major influence over all the fitness criteria, as it affects 5 of them. In fact, the FC5 (Total Height of the Tower) is only controlled by the Gene5. The gene with the minor effect is the Gene4, which is the polygon rotation (polygons on the core and the facade).

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SIMULATION 1 / BEST RANK INDIVIDUALS

The first simulation run with 50 generations with 10 individuals each. The best fittest individuals to the criteria first evolved at Generation 20. They appeared again after Generation 34 for the other 4 fitness criteria, showing that most of the best solutions are generated towards the end of the experiment, and the solutions showed a convergent behavior.

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SIMULATION 1 / OBJECTIVE SPACE

The objective space of the Simulation 1 for the entire population showing the individuals by comparing them due to 5 different fitness objectives. Since the 3-dimensional charts are unable to illustrate more than 3 objectives on the 3 axes, the color code and the size also used as indications of the fourth and fifth criteria. For the FC4, which is the maximum terrace area on the south, smaller cube defines a better solution, where for the FC5, that is the maximum tower height, blue color shows optimized solutions (within the gradual color change from red to blue). The second graph on the diagram above displays the individuals on the 3-dimensional objective space from the side view (FC1-FC3 axes). On the side view, it can be observed that some individuals can be optimized in a single criterion while getting distant from the best solutions for the remaining criteria, as marked in the graph. As the golden boxes represent the Pareto Front, their locations in the objective space can be observed critically. Since the locations are creating a concave curvature that was going towards the center, it shows that the solutions are being optimized while generating friction between the conflicting objectives.

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SIMULATION 1 & 2 FITNESS VALUE AND SD GRAPHS/ BEST RANK INDIVIDUALS

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SIMULATION 2 / PARALLEL COORDINATE PLOT

On the fitness value graphs (on the previous page), each horizontal polyline represents a generation while each node stands for an individual. As each graph shows only one fitness criteria, the graphs can be used to evaluate the trend of individuals for 5 different fitness criteria. For all criteria, the graphs have a similar trend of fluctuation and shifting of the blue polylines (that represents later generations) to the bottom of the graphs. From Simulation 1 to 2, the experiment focused on the removal of Fitness Criteria 3. The Fitness Value and Standard Deviation Value graphs are showing the change in the optimization process clearly. It can be observed that all the Fitness Value graphs converged towards the bottom, and the fluctuation is minimized. For the SD graphs, again, the optimization trend can be seen as the diverse graphs are replaced with the converged and left-sided graphs. As can be read from the graphs, the experiment succeeded in a way that the effect of the removal of particular fitness criteria is observed clearly. In the Parallel Coordinate Plot of Simulation 2, tex represents a fitness criterion. Compared to allows the observation between the objectives, (first individuals of the generation) are showing showing that the fitness criteria 1 is conflicting

each line shows an individual while each parallel verthe SD and FV Graphs, the Parallel Coordinate Graph to see if they are conflicting or not. The red lines a zig-zag pattern at the beginning of the simulation, with FC2 and FC4 while going consistent with the FC3.

To observe change, the morphological characteristics that are explored through the generations, Generation 10, 30, and 49, are plotted as plan drawings. They are color-coded, as a gradual change from the red to blue, to match with the Parallel Coordinate Plot. The gradual variety of the morphology in terms of the change in polygon type and the size is visible towards the end of the simulation.

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SIMULATION 2 FITNESS BEST & WORST RANK INDIVIDUALS

The diagram shows the best and the worst individuals for each fitness criteria and allows the observation for the variety in form and geometry. The simulation explored 2 far extremes for each fitness criteria. When all the FC are analyzed, it can be said that the geometric variation between the best and the worst rank individuals for FC3 is more extensive compared to other fitness criteria. So, the morphological characteristics are explored widely during the simulation for FC3. On the other hand, FC4 has shown the narrowest variation of form difference. In FC1 the significant difference between the individuals is the height but not the form characteristics. So, the pre-defined genes and the simulation setup explored more for the FC2 and FC3 compared to the others. Besides, as the forms are observed, the trend in height is the same for all best and worst solutions where the highest towers are selected as the best rank individuals.

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SIMULATION 2 / PARETO FRONT OF THE ENTIRE POPULATION

The diagram above showing all the Pareto Front individuals of the entire population, that are clustered as generations. The similarity on the form can be observed from the graph that indicates a convergence on specific geometrical properties such as the maximum height of the towers and mostly triangular floor plan layout.

OBJECTIVE SPACE

In the objective space, pareto fronts (represented by golden cubes) are densified closer to the origin that shows the optimization. Besides, the separation between the individuals as 3 different families are obvious in the graph.

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FIRST 100 INDIVIDUALS OF THE POPULATION

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SEQUENCE 2 /EXPERIMENT 01 CONCLUSION

The first experiment of Sequence 2 is focused on extracting the basic geometric and architectural principles of the IFC Tower and creating a primitive tower geometry accordingly. The major exploration in the experiment was on the possible floor plan layouts, atrium formation, and the facade generation. As it also visualized in the previous pages, â&#x20AC;&#x2DC;First 100 Individuals of the Populationâ&#x20AC;&#x2122;, there exists a wide range for the morphological varieties in the form. One of the key observations in the experiment was to observe the effect of the removal of a fitness criterion. The first simulation was not successful in terms of optimizing the solutions with 5 fitness criteria. However, in the second simulation, after the third fitness objective is extracted from the experiment setup, all the other criteria succeeded in optimizing the solutions. As a general trend, the given objectives and the setup generate such optimized individuals that tend to be the highest (in the rank of the height gene) and showing a narrow range of variety on the tower geometry (either triangular or rectangular floor layout and mostly regular openings on the shading element). As a critical reflection, the multi-objective evolutionary algorithm is used mostly to explore the possible tower forms within a given setup. So the focus of the experiment was basically on the geometry and form rather than environmental factors of function. As a further development of this experiment of Sequence 2, some other criteria could have been explored as well.

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SEQUENCE 2 /EXPERIMENT 02 CONCLUSION

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URBAN CONTEXT AND CONNECTIVITY INTRODUCTION The final experiment setting is created in the environmental context of the city Guangzhou in China. The IFC Tower is located in a commercial district upon the Zhujiang River. The urban planning of the district is centralized towards a linear axis, which connects the commercial towers with a green corridor. However, this distribution lacks sufficient connectivity and green area, besides the network is focused mostly on vehicle access. Thus, the experiment is set up to provide enhanced connection between the commercial towers, and within the whole area, while allowing more green space that is integrated yet distributed. The location of the tower, which stands as a landmark, serves a node role to connect the right and left north corners of the district to riverside via pedestrian-friendly green corridors surrounded by towers. In the new planning, the main vehicle access is protected while the secondary roads are transformed for pedestrian usage. To provide an uninterrupted network for pedestrians, a multi-node bridge is also integrated into the system.

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PSEUDO CODE FOR SETUP

The urban setup is created first, by dividing the whole district into 9 regions, with 4 axes for vehicle access (1 & 2). Each region is further divided into a number of parcels ranging from 20 to 50 (3). The largest parcel among all, within the 9 regions, is selected to be the location of the tower (4). Towards the tower, 3 axes are drawn from the right and left north corners of the region, and the middle point of the riverside (5). Through these 3 axes, green corridors are distributed on the intersected parcels with the axes (6). The distribution of the commercial towers is controlled by the closest parcels (ranging from first 30-80 closest parcels) to the green corridors (7). As for the residential and the social buildings, the remaining parcels are selected (8). As a critical reflection, towards the end of the experiment, it is observed that this method of distributing the building functions resulted in the lack of diversity through the axes and the isolation of the residential and social buildings on the corner regions of the district. The approach for the urban setup will be discussed in the conclusion section. Additional genes for the setup are: Parcel number in each region Commercial tower offset from its parcel Cultural & residential building offset from its parcel Cultural & residential building courtyard formation due to the height Facade shading curve vertex & number

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ADDITIONAL GENES

4) Facade shading curve vertex

5) Facade shading curve number

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BODY PARTS

As an addition to the Sequences 1 & 2, the body parts of the experiment are increased in number. Besides the tower, other body parts are selected from the urban context as commercial buildings, cultural & residential buildings, and the green areas. Within this setup, the level of detail/complexity for the body parts of the tower is decreased (compared to Sequence 2), in order to run the simulation for the tower and the context together. However, this resulted in the lack of connectivity with the tower and the other buildings and the context. Instead, the experiment is focused more on the location of the tower rather than the architectural/geometrical features of it. This will be explained in more detail in the conclusion section.

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FITNESS CRITERIA 1: MAX URBAN CONNECTIVITY VIA GREEN COURTYARDS

The fourth fitness criteria are the maximum connectivity of the commercial & cultural buildings via the green courtyards. The buildings are divided into 4 segments, and their heights are controlled separately. Once the height of any segment decreases to the limit of 10m, the segment is eliminated and creating an open courtyard instead. The connection of the courtyards within themselves is not well set, and this will be one of the developments that will take place in the conclusion part.

FITNESS CRITERIA 2 & 3: MAX GREEN AREA & WALKING DISTANCE THROUGH GREEN

As the new body parts are added into the experiment, that required additional fitness criteria as well. First of the fitness criteria is to be the maximum total area of the green parcels (The effecting genes of the criteria are the number of the cells in each region and the offset values of the other buildings as the offset gap is forming green area on each parcel as well). Followed by the second criterion, which is the maximum walking distance through the green corridors towards the tower, whereas the requirement of the minimum walking distance from the tower to the riverside is also needed. The effecting gene is the cell number in each region, as it determines the location of the tower and the green corridor as well. The second gene aims to locate the tower towards the southern region of the district as it optimizes the desired walking distances. Emergent Technologies and Design / AA School of Architecture

FITNESS CRITERIA 4: MAX SHADING OF TOWER ON GREEN AREA

The third of the fitness criteria is to be the maximum shading of the tower on the green areas. This criterion is set in order to provide sufficient shadow for the pedestrian on the green corridors and also to optimize the position of the tower, which in this case will be positioned towards the middle point in the horizontal axis. Hypothesis: If the simulations show convergence towards the best fittest solutions, the location of the tower will be situated on the mid-south of the district, since 3 out of 5 genes in total direct it to be so. Individuals with the opposite behavior of tower location will be deduced carefully through the experiment in order to understand the effecting genes on them.

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FITNESS CRITERIA 5: MAX SHADING ON THE SOUTH FACADE

The last fitness criteria are the maximum shading on the south facade of the tower, which the genes of facade shading curve vertex & number effecting. As a critical reflection, it can be noted that the fitness criteria and the effecting genes for the tower are not integrated with the context; this results in the lack of connection of the tower to its surroundings. In conclusion, the connection proposals will be explored.

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SIMULATION 1

BEST RANK INDIVIDUALS

SIMULATION 1 STANDARD DEVIATION

SIMULATION 1 FITNESS VALUES

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SIMULATION 1

The first simulation runs to generate 50 generations with 10 individuals each. Best ranked individuals are observed for every 5 criteria, and the most obvious trend was the position of the tower that is generated on the south side of the district. The only individual that went against the trend was of criteria 3, which is the maximum walking distance from the north and minimum distance towards the south. So, the expected result was the opposite, which the tower to be located towards the south. Another crucial observation here was that the best fittest individuals for criteria 1 and 2 were almost the same, even though they appeared at the distant generations within the entire population (consecutively generation 45/individual 7 and generation 26/individual 3). Although the individuals are almost identical, the standard deviation and the fitness value graphs show differences. For criteria 1, the graphs showing gradual convergence, wherein the criteria 2 the convergence is not gradual but more distributed. Besides, the fourth and fifth criteria were failed in the optimization process, as can be seen from the SD graphs, in which the individuals show almost no convergence and extremely large distribution over the graph in criteria 4 and just slightly convergence yet mostly distributed behavior in criteria 5.

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SIMULATIONS 2&3 SIMULATIONS 2 BEST RANK INDIVIDUALS

SIMULATIONS 3 BEST RANK INDIVIDUALS

SIMULATION 2 SD GRAPH

SIMULATION 3 SD GRAPH

SIMULATION 2 SD VALUE GRAPH

SIMULATION 3 SD VALUE GRAPH

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SIMULATIONS 2&3

After the first simulation, some of the computational processes that effects the fitness criteria are changed. Criteria 3,4, and 5 were not showing successful solutions and graphs, so the changes made mostly on these parts, and some errors are fixed. Simulations 2 & 3 (both 50 generations with 10 individuals each) were run exactly with the same settings in different computers. The aim in this experiment was to see the effect of the â&#x20AC;&#x2DC;randomnessâ&#x20AC;&#x2122; in the whole evolutionary algorithm process, even within the same conditions. The best rank individuals for the criteria 2 & 3 were almost identical, and the Standard Variation graphs have the same trend of starting from distributed values and converging in a narrow range towards the end. However, the SD Graphs were behaving differently for both criteria, in which simulation 3 shows less fluctuation for the second and third criteria. For the first, fourth, and the fifth fitness objectives, the best-fitted individuals are quite similar, yet not identical. The first objective, by means of the SD Graph, is getting optimized in simulation 3. However, it shows a distributed trend for simulation 2 that can be called as a successful result by also analyzing the SD Value graph, which fluctuates and not getting stabilized. Through the analysis of simulations 2 & 3, the results were debatable in terms of success as they did not optimize all. In order to set up another experiment to achieve more desired results, computational changes on the algorithm were not sufficient. So, the number of fitness objectives decided to be changed and aimed to observe how this can alter the overall results.

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Pareto front of the entire population of Simulation 2 is shown. The major trend appears on the individuals is the location of the tower, which positions towers towards the mid-south of the district. The hypothesis made in the section â&#x20AC;&#x2DC;Fitness Criteria 3â&#x20AC;&#x2122; is partially proven (not completely) with the pareto fronts since the tower position also shifts to the right-north side of the boundary for the significant number of individuals (although the majority is located on mid-north).

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FITNESS CRITERIA 6: URBAN CONNECTIVITY VIA BRIDGE

In order to generate a multi-node bridge, 6 points are selected within the range of 9 regions. Nodes are created between the intersection of 3 neighbor points to create an uninterrupted connection for pedestrians between the regions. The distance between the center of these 6 points and the location of the tower is measured as value A. Green areas are divided into points by a regular grid, and the points that intersect with the bridge are selected. The average distance between the selected points and the tower is calculated (as value B). For the connectivity fitness criteria, the ratio of A/B is taken. The criteria aims to minimize the relative distance between the bridge and the tower (that functions as a node for transportation and meeting) while maximizing the connection range of the bridge within the regions. Effecting genes: The number of pinching points Number of cells in each region

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Variety of individuals with bridge:

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SIMULATION 4 Best rank individuals:

SIMULATION 2 SD VALUE GRAPH

SIMULATION 3 SD GRAPH

Simulation 4 was the last experiment of Sequence 3, which aims to investigate the effect of the 6th additional fitness criteria to the experiment setup. The individuals above illustrating the best rank solutions for each criteria. The most obvious similarity on those individuals is the bridge geometry, which appears exactly the same for all. On the previous page, â&#x20AC;&#x2DC;Variety of individuals with bridgeâ&#x20AC;&#x2122;, even though we can observe a range of phenotypes of the bridge, the experiment converges the solutions to a 3-node bridge.

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PARETO FRONTS OF THE ENTIRE GENERATION

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SIMULATION 1 STANDARD DEVIATION (FC 1-5)

SIMULATION 1 FITNESS VALUES (FC 1-5)

SIMULATION 2 SD VALUE GRAPH (FC 1-5)

SIMULATION 2 SD GRAPH (FC 1-5)

SIMULATION 3 SD VALUE GRAPH (FC 1-5)

SIMULATION 3 SD GRAPH (FC 1-5)

SIMULATION 2 SD VALUE GRAPH (FC 1-6)

SIMULATION 3 SD GRAPH (FC 1-6)

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SEQUENCE 3 CONCLUSION In the fourth simulation, the integration of the 6th fitness objective was the major difference from the previous experiments. So, the effect of additional objective on the evolutionary computational process is observed in the last simulation. In the case of the first objective, which is the ‘urban connectivity via courtyards’ the SD graph has a gradual diverging trend showing that the data getting distant from being optimized, compared to the Simulation 3, where the SD graph was converging and shifting towards left steadily. The SD Value graph is also showing an increase and fluctuation in portraying the wide distribution of solutions. For the second objective (maximum green area), although the 1st, 2nd, and 3rd Simulations resulted in successful and optimized solutions, at the last experiment SD graph shows a highly distributed trend. Computationally, the calculation process of the second objective has been the same from the beginning of Sequence 3. The reason that the second criteria changed its behavior could be the inclusion of the 6th objective so that the optimization process is effected. The third fitness objective has been showing convergence and been optimized since the first simulation. As it also converged and optimized through shifting towards the left side on the SD graph, the ‘walking distance’ succeeded as a criterion in all the experiments. The maximum shading of the tower on the green areas, which is the fourth fitness objective, started as a deviated graph that failed in optimizing the solutions. However, on the 2nd and 3rd simulations, the graphs illustrated optimized and converged solutions, even though they started from diverse solutions. Optimization of the south facade shading has always been distributed in the first 3 simulations. Exceptionally, in the third simulation, the data gradually concentrated to a narrower range and shifted towards the left-hand side of the graph, while the decrease in the SD Value graph supporting the optimization. However, the simulation 4 was not as successful as the simulation 3, yet it was only able to shift the data to a smaller value without converging. Urban connectivity via bridge was the last fitness criteria that added to the experiment setup on the 4th simulation. It is observed that the inclusion of this last criteria had an effect on the other objectives. The major effect is marked on the FC 2 (maximum green area), which resulted in the scattered data compared to the previous experiments. The least effect could be on FC 3, which is the optimization of walking distance towards the tower and from the tower, as it succeeded in all the experiments. The FC 6 itself has shown a shift towards the right-hand-side of the SD graph, in which the behavior is observed for the first time in Sequence 3.

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CONCLUSION 1 General Reflection The whole process of evolutionary algorithms generated varieties of design solutions of the primitive geometry for a given set of problems. The problems, called as fitness objectives in the experiments, had a wide range of scales and functions. From Sequence 1, the complexity of the given problem and the scale and the of the fitness criteria (as well as the scale of the environmental factors) gradually increased until Sequence 3. On Sequence 3, the experiment is held in a broader area. The effect of the addition of bridge geometry (and the fitness objective to optimize the connectivity of it) over the existing setup and the fitness objectives is observed. Additionally, environmental objectives such as the riverside, existing commercial district, sun direction are integrated into the setup as well. In the overall experiment setup, the major focus was on the density and the arrangement of the functions on the area, rather than the concentration to the connectivity of those functions. Even for the tower, the location of it was the major criterion as it was working as a node. However, more architectural factors could have been explored for the tower. Besides, for the connectivity of the different functions, the strategy of distribution could be set with more control (because the distribution was not even and residentials buildings were isolated). Additionally, in order to enhance the variety of objectives and not limit them with only the land use distribution, ladybug plug-in could have been used to measure the possible desired views from the crucial locations in the area. As a critical reflection, generative algorithm evolved possible solutions for a given design problem, even though it was able to optimize the solutions, the algorithm was not successful in a way that it did not produce the exact desired solutions. The limitation of the GA was that the definition was not controlled completely thus lacked of producing accurate solutions. For instance, in Sequence 3, the bridge definition couldnâ&#x20AC;&#x2122;t be set precisely and the bridge geometry was passing through the buildings (the grid for the bridge nodes was the limitation that couldnâ&#x20AC;&#x2122;t be controlled). Additionally, while constructing the network system within the buildings and greenery, Voronoi geometry is used. However, it also has the constraint to be in a regular region, thatâ&#x20AC;&#x2122;s why the region boundaries remained as regular rectangles. For the exploration of the environmental conditions, the shortest walking distance was useful to have a pedestrian-friendly region. However, further developments for environmental conditions can be held, such as CDF analysis. All in all, the GA was useful to generate thousands of potential problem solutions even though it had various limitations. Some potential further developments to overcome with the encountered limitations are mentioned in the following part of the conclusion.

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FURTHER DEVELOPMENTS 2 Deformation of the regular grid of regions The commercial district in the city of Guangzhou, China, was aimed to set up in a regular grid that allows enhanced transportation, connectivity and greenery in Sequence 3. However, due to the 9-segment regular grid, the experiment setup lacked variety in the arrangement of functions and cell size/organization. For further developments in the setup, the regular grid could be generated in variations to allow a range of possible network systems. If the case of the irregular grid geometry, the cell sizes, bridge variation, and connectivity principles of the experiment setup would show a high range of variations, with all these variations, the change in the optimization process can be observed, as well as it will allow experimenting the disorders in the cities.

Existing grid formation

Possible grid geometries

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REFERENCES

Darwin, C., 1859. On the Origin of Species by Means of Natural Selection. London : John Murray, 1859.

Natural Selection: Charles Darwin & Alfred Russel Wallace. Accessed February 15, 2020. https://evo lution.berkeley.edu/evolibrary/article/history_14. 2. “A Brand New City Landmark Now Opens to Public.” LIFE OF GUANGZHOU. Accessed February 10, 2020. https://www.lifeofguangzhou.com/wap/knowGZ/content.do?contextId=7458&frontParentCata logId=175.

BIBLIOGRAPHY Coello, C.C., 2006. Evolutionary Multi-Objective Optimization: A Historical View of the Field. IEEE Comput. Intell. Mag. 1, 28–36. Makki, M., Showkatbakhsh, M., Tabony, A., and Weinstock, M., Evolutionary Algorithms for Generating Urban Morphology: Variations and Multiple Objectives, International Journal of Architectural Computing (2018) 1-31 Zitzler, E., Thiele, L., 1998. An Evolutionary Algorithm for Multiobjective Optimization: The Strength Pareto Approach. Citeseer. Beasley, D., Bull, D.R., Martin, R.R., 1993. An Overview of Genetic Algorithms : Part 1, Fundamentals. Univ. Comput. 15.

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ARCHITECTURAL ASSOCIATION SCHOOL OF ARCHITECTURE GRADUATE SCHOOL PROGRAMMES COVERSHEET FOR SUBMISSION 2020-21

PROGRAMME:

Emergent Technologies & Design

STUDENT NAME(S): Berin Nur Kocabas Jong In CHOI

SUBMISSION TITLE

Evolutionary Tower and Urban Design Approach

EMERGENCE AND EVOLUTIONARY COMPUTATION MULTI OBJECTIVE EVOLUTIONARY ALGORITHMS COURSE TITLE COURSE TUTOR

Elif Erdine, Milad Showkatbakhsh

DECLARATION: “I certify that this piece of work is entirely my/our own and that any quotation or paraphrase from the published or unpublished work of others is duly acknowledged.” Signature of Student(s):

Date:

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