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An Evolutionary Design Process – Adaptive-Iterative Explorations in Computational Embryogenesis

Authors:
AN EVOLUTIONARY DESIGN PROCESS
Adaptive-iterative explorations in computational embryogenesis
Vignesh KAUSHIK and Patrick JANSSEN
National University of Singapore, Singapore
vigneshkaushik@gmail.com, patrick@janssen.name
Abstract. Computational embryogenies are a special kind of genotype to
phenotype mapping process widely used in explorative evolutionary sys-
tems as they provide the mechanism for generating more complex solutions.
This paper focuses on how designers explore embryogenies for specific
design scenarios through an adaptive-iterative process. The process is
demonstrated for a complex project to generate a prototypical urban farm in
Singapore. It is shown that by employing an adaptive-iterative process, the
embryogeny can be made progressively more complex and less abstract,
thereby allowing the exploration to be guided by the designer.
Keywords. Computational embryogeny; evolutionary; multi-criteria opti-
mization; encoding; decoding.
1. Introduction
Evolutionary design algorithms evolve populations of designs by iteratively
applying a set of procedures to design variants in the population (Frazer, 1995).
With the evolutionary design approach, the parameters for a design variant are
referred to as the genotype (with each individual parameter being a gene), the
model of the design variant is referred to as the phenotype, and the evaluations of
the design variant are referred to as performance scores. Three key procedures
need to be defined: a developmental procedure that generates a phenotype from a
genotype, one or more evaluation procedures that calculate performance scores,
and a feedback procedure that performs genetic reproduction based on the per-
formance scores.
For designs with limited variability, the developmental procedure can use direct
parametric modelling. However, in cases where greater variability is required, the
developmental procedure may require a more complex transformation process.
R. Stouffs, P. Janssen, S. Roudavski, B. Tunçer (eds.), Open Systems: Proceedings of the 18th International
Conference on Computer-Aided Architectural Design Research in Asia (CAADRIA 2013), 137–146. © 2013,
The Association for Computer-Aided Architectural Design Research in Asia (CAADRIA), Hong Kong, and
Center for Advanced Studies in Architecture (CASA), Department of Architecture-NUS, Singapore.
137
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These complex types of developmental procedures are commonly referred to as
‘computational embryogenies’ (Bentley and Kumar, 1999).
Embryogenies are a special kind of genotype to phenotype mapping process
widely used in explorative evolutionary systems as they provide the mechanism
for generating more complex solutions. This paper focuses on how designers
explore embryogenies for specific design scenarios through an adaptive-iterative
process. Previous research by Bentley and Kumar (1999) on computational
embryogenies has typically focused on their behaviour and efficiency of search
and their scalability for evolving different morphologies. More recently, both
Dillenburger et al. (2009) and Janssen and Kaushik (2013) have proposed evolu-
tionary design approaches for the automatic arrangement of building volumes on
a given site. Although generative and evolutionary design is an on-going research
topic, little attention has been paid to the process of how designers might create
such embryological procedures for specific design scenarios.
As a demonstration of this process of exploring an appropriate embryogeny,
this paper presents a case-study of creating an embryogeny for generating com-
plex spatial configurations for a thesis project at the National University of
Singapore.
In the case study example presented in this paper, the process of constructing
an embryogeny involved four versions of adaptive-iterative exploration, each
described in more detail in Section 4.
2. Design Scenario
The design scenario focuses on the need for Singapore to become more self-suffi-
cient in terms of food production, since it imports over 90% of its food
requirement (Osman, 2011). Hence, as a long term strategy to ensure food
resilience for Singapore’s growing population, a prototypical urban farm typology
catering to a population of 10,000 people is proposed. The idea would be to have
a decentralised network of such urban farms across various parts of Singapore.
State-of-the-art farming methods would allow essential food items such as veg-
etables and fruits, fish and chicken to be grown vertically and simultaneously
within the same building. The urban farmers who grow the food were to live
within the same complex as well. Also, in order to produce a part of the energy to
power the building, it was decided to grow algae (bio-fuel) on photo-bio-reactor
pipes to be fixed to the parts of the façade receiving the most solar radiation. The
market, seed storage, waste recycling centre and many other allied spatial systems
were to be located within the complex.
Each of these spatial functions has individual daylight requirements and adja-
cency rules that must be satisfied for its optimum functioning. For example, the
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vegetable growth chamber must have adequate daylight, whereas the chicken
growth chamber requires controlled daylight. The latter should preferably be far
away from the farmers’ housing but closer to the markets. The fish growth cham-
bers need to be at lower floors for structural reasons but as close to the vegetable
growth chambers as possible. Such complexity of the spatial and functional inter-
relationships and constraints means that a complex embryogenic type of
developmental procedure is required.
2.1. TOOLS
The developmental and the evaluation procedures were defined using a Visual
Dataflow Modelling (VDM) approach using Houdini, an advanced procedural
CAD application (Janssen and Chen, 2011a). Modelling in a VDM system consists
of creating dataflow networks using nodes and links. Each node can be thought of
as a function performing an action and a link is used to connect the output of one
function to the input of another function. VDM systems are increasingly being
used as an important tool in performance-based design approaches (Shea et al.,
2005; Coenders, 2007; Lagios et al., 2010; Toth et al., 2011; Janssen and Chen,
2011b; Janssen and Kaushik, 2012).
The evolutionary algorithm was executed using Dexen, a distributed execution
environment. The feedback procedure, generated automatically by Dexen, will
rank groups of phenotypes using a standard Pareto ranking method, and will then
create new genotypes using standard crossover and mutation operators (Janssen
et al., 2011).
3. Exploring Embryogenies
An embryogeny is a process of growth that defines how a phenotype is generated
from a genotype. For example, in nature, embryogenies indicate how an animal
should be grown. Hence the genotype may be regarded as a set of ‘growing
instructions’, or a recipe that defines how a phenotype will be developed.
Current computational embryogenies can be classified into three different
types: external, explicit and implicit (Bentley and Kumar, 1999). External
embryogenies can be imagined to be a piece of computer code that performs a one
to one mapping from genes to parameters and the process is not generally evolved.
An explicit embryogeny is where every step of the growth process is explicitly
specified as instructions in the data structure. Typically, the genotype and the
embryogeny are combined and are allowed to evolve simultaneously. The third
type, the implicit embryogeny, does not explicitly specify each step of the growth
process, but is implicitly coded by a set of rules or instructions. By evolving a set
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of simple rules which can then be iteratively applied to each element of the grow-
ing solution, it is believed that many large scale problems can be tackled.
For the purpose of this design scenario, versions of varying complexities of
external embryogenies were used for evolutionary exploration. These versions
became progressively more complex and less abstract. One of the reasons for
using external embryogenies is that the user retains more control over the final
evolved form. Moreover, one can constantly improve the quality of evolved
designs by making careful modifications to the embryogeny. However, one must
ensure that this complex mapping process will always produce a legal phenotype
and overcome a common issue with such complex developmental procedures,
referred to as the variability problem (Janssen, 2004).
3.1. VERSION 1
For the first exploration, the design problem discussed above was split into a set
of cubes that were allowed to float on an abstract site of 250m by 250m. Each cube
represented a fragmented part of the various functions in the urban farm and was
spatially positioned within a 3d grid, as shown in Figure 1.
The embryogeny used a direct representation for defining the positions of each
of the cubes within the 3d grid. The genotype consisted of a set of real-valued
140 V. KAUSHIK AND P. JANSSEN
Figure 1. Floating cubes – version 1 of embryogeny exploration.
2A-186.qxd 4/28/2013 9:26 AM Page 140
genes in the range {0,1}. For each cube, the position was defined by three genes,
which were mapped to a 3d coordinate position in the grid. No constraints what-
soever were set and hence cubes were allowed to float in space and multiple cubes
were allowed to occupy the same position in the grid. Each phenotype was then
allocated an overall fitness score by evaluating certain simple adjacency rules for
each cube. The performance criteria for the evolutionary algorithm were to max-
imise the number of cubes satisfying the adjacency rules. An example of one of
the design variants is shown in Figure 1.
The aim of this experiment was to acquire an overall understanding of the behav-
iour of the cubes under the influence of the various adjacency rules. It was expected
that the unconstrained freedom of position of each cube relative to all others would
allow a variety of promising spatial patterns to be identified. However, although the
exploration produced many phenotypes that satisfied most of the adjacency rules, it
was very difficult to evaluate them visually and understand their behaviour due to
their high variability. The main reason identified for the chaotic variability was the
lack of constraints. Hence, in the next stage, additional constraints were introduced.
3.2. VERSION 2
In version 2, two constraints were introduced: cubes were not allowed to float, and
multiple cubes were not allowed to occupy the same position in the grid. The same
genotype representation was used, consisting of three real-valued genes in the
range {0,1} for each cube. Two genes were mapped to a 2d coordinate position in
the grid and the third gene was used to define the stacking order of the cubes. All
the cubes with the same 2d coordinate were sorted according to the stacking gene,
and were then stacked in that order, starting from the ground up. An example of
one of the design variants is shown in Figure 2.
As expected, certain patterns and groupings emerged among the various func-
tions. However, the variability among the solutions remained chaotic. Two key
deficiencies were identified: the vertical circulation of the cubes was not clear due to
the lack of cores, and the floor areas required for each function was not constrained.
3.3. VERSION 3
From the solutions generated from the previous versions, it was decided to group
certain functions into a tower and podium typology. The tower consisted of four
functional types; farmer’s housing, vegetable farms, chicken farms and fish farms.
All other allied functions were grouped together to form the podium. It was also
decided to opt for a smaller site of 150m by 180m, with real surroundings that
would have a bearing on the evolution of a podium and tower building. For the
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purpose of simplifying the search space, the focus of version 3 was only to evolve
the tower, assuming a fixed podium block as a base of the tower.
The tower’s core consisted of four independent sub-cores (one for each func-
tion in the tower) with each catering to its respective part of the floor plan. These
sub-cores were structurally integrated but functionally independent. This ensured
that at any floor level, functions could be arranged in a flexible manner.
The developmental procedure used a combinatorial parametric modelling tech-
nique for generating the floor plans. Each floor consisted of four rectangles of
varying sizes, with different functions assigned to each rectangle. The genotype
consisted of a total of 240 genes, 12 genes per floor and three genes per rectangle.
One gene was used to select the shape of the rectangle from a set of predefined
possibilities; one gene was used to indicate the orientation of the chosen rectangle
around its sub-core, and one gene was used to select the function of the rectangle.
Three variants of the floor plan configuration are shown in Figure 3.
The fitness scores were based on a number of evaluation criteria, such as suf-
ficient daylight for the food growing chambers and the farmer’s housing, scenic
views and privacy for the housing, and percentage of façade that could be utilised
for growing algae with photo-bio-reactor pipes.
It was also decided to add a constraint that would limit the floor areas assigned to
each of the four functions. The required floor areas are known in advance, and a tech-
nique was therefore required for defining this as a constraint within the evolutionary
142 V. KAUSHIK AND P. JANSSEN
Figure 2. Stacking cubes – version 2 of embryogeny exploration.
2A-186.qxd 4/28/2013 9:26 AM Page 142
system. Researchers have identified four main approaches to handling constraints in
evolutionary algorithms: 1) penalty functions 2) repair functions, 3) specialised repro-
duction operators, and 4) specialised genotype to phenotype decoder functions (Eiben
and Smith, 2008). For this version, a penalty function was added that reduced the fit-
ness of those solutions where the area assigned to each function was not desirable.
The solutions from the evolutionary exploration of this version indicated that
certain aspects of a tower design such as the structural grid had to be refined. In
addition, the penalty function resulted in many variants with very low scores,
which degraded the ability of the evolutionary algorithm to evolve high perform-
ance designs. Figure 4 shows three variants from the population of evolved designs.
3.4. VERSION 4
After analysing the results from the previous version, it was decided that the size
and shape of the floor plan should be defined in multiples of a specific structural
AN EVOLUTIONARY DESIGN PROCESS 143
Figure 3. Variants of floor plan configuration.
P-farmer’s housing, V-vegetable farm, C-chicken farm, F-fish farm
Figure 4. Phenotypes from version 3 of embryogeny exploration.
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grid. After careful analysis, a structural grid of 7.2m x 7.2m was adopted as it was
found to be well suited for all the four functions in the tower.
It was also noted that the use of a penalty function for constraining the floor
areas of the different functions was not successful. In this version, this constraint
was embedded within the developmental procedure, using a rule based decoder
function that combined direct and indirect mapping of genotypes to phenotypes.
This approach falls under the fourth category of constraint handling techniques as
described by Eiben and Smith (2008). In this case, the genotype to phenotype
mapping process was structured as a sequential chain of decisions (for more
details see Janssen and Kaushik, 2013).
Each floor consisted of four rectangles of varying sizes, each overlapping with
one of the sub-cores. The dimensions of each rectangle were defined as a multiple
of 7.2m. As with the previous version, the rectangles were assigned different func-
tions. The genotype consisted of a total of 180 real-valued genes in the range
{0,1}, 9 genes per floor. The first 8 genes were used to specify the dimensions of
the rectangles. The genes were mapped to integer values in the range {0,4} (result-
ing in a maximum of four grids of 7.2m each from the edge of the core). This
mapping procedure is shown in Figure 5.
The ninth gene was used to select how the different functions were assigned to
the four rectangles. In total, there were 21 different ways of assigning functions to
the rectangles. Six of the 21 configuration are shown in Figure 6. However, in
order to constrain the floor area assigned to each function, a filtering process was
first performed. Those configurations that would result in excess floor area being
assigned to any of the four functions were filtered out as being invalid. The ninth
gene was then used to select one of the remaining valid configurations by map-
ping it to an integer value in the range {0,n}, where n was the total number of valid
configurations. This ensured that all design variants were assigned approximately
the required floor areas for the four functions.
144 V. KAUSHIK AND P. JANSSEN
Figure 5. Developmental procedure of version 4 of embryogeny exploration.
P-farmer’s housing, V-vegetable farm, C-chicken farm, F-fish farm.
2A-186.qxd 4/28/2013 9:26 AM Page 144
In this version, the control of variability was improved significantly and as a
result the evolutionary exploration produced solutions with better performance.
Figure 7 shows three examples of evolved designs.
4. Conclusion
This paper focussed on the process of creating embryogenies for a complex design
problem through a sequential process of adaptive-iterative exploration. At each
AN EVOLUTIONARY DESIGN PROCESS 145
Figure 6. Few options of assigning the functional configurations.
P-farmer’s housing, V-vegetable farm, C-chicken farm, F-fish farm
Figure 7. Phenotypes from version 4 of embryogeny exploration.
2A-186.qxd 4/28/2013 9:26 AM Page 145
version, the solutions that were evolved were not interpreted as optimal answers,
but as diagnoses of potential problems and as suggestions for further architectural
explorations (Caldas et al., 2001). Also as Dawkins (1988) points out, certain
embryogenies are better than others at producing certain morphologies and hence
it is essential for designers to explore such trade-offs through a trial and error
process. By employing an adaptive-iterative process, the embryogeny can be made
progressively more complex and less abstract, thereby allowing the exploration to
be guided by the designer.
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... The computer will execute a new search process, thereby producing more optimized designs. This can continue until the designer is satisfied that they have developed a reasonable understanding of the space of design possibilities (Chen et al., 2021;Wang et al., 2019;Kaushik and Janssen, 2013). ...
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... However, setting up an urban design optimisation is a complicated technical process, in which the setup needs to link many domain specific applications and automate their execution. Currently, the solution to overcome this technical obstacle is embedding the optimisation process within a Computer-Aided Design (CAD) application such as Rhinoceros3D Grasshopper (Koenig and Varoudis (2016); Taleb and Musleh (2015)), Generative Components (Mueller and Strobbe (2013); Turrin et al. (2012)), Dynamo Revit (Rahmani Asl et al. (2015)) and Houdini3D (Kaushik and Janssen (2013)). The solution requires users to construct the 3D model in the CAD application; the geometries from the 3D model are processed and passed to domain-specific applications for analyses, which are usually linked to the CAD application as plug-ins. ...
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"October 2004" Thesis (Ph.D.)--The Hong Kong Polytechnic University, 2005. Includes bibliographical references.
A generative design system applied to Siza's school of architecture at Oporto
  • L Caldas
  • J Rocha
Caldas, L. and Rocha, J.: 2001, A generative design system applied to Siza's school of architecture at Oporto, in J. S. Gero, S. Chase and M. Rosenman (eds.), Sixth Conference on Computer-Aided Architectural Design Research In Asia (CAADRIA'01), Sydney, Australia, 253-264.