Evolutionary Algorithms have been used extensively in recent years to mimic the principles of evolutionary science to solve common real-world problems through search and optimisation procedures of single or multiple objectives, a process that is commonly known as Generative Design in the AEC industry. Ranging from the fields of economics to politics and music to architecture, evolutionary algorithms have proven to be an efficient problem-solving technique to find multiple trade-off solutions for problems that possess multiple ‘fitness criteria’ (objectives) that conflict with one another. The seminar aims to introduce the concepts of multi-objective optimisation and develop an understanding of their application in design, primarily through the development of urban tissues. The seminar provides the necessary knowledge of biological principles of evolution and thoroughly elaborates on their impactsin the design process through the implementation of evolutionary computation in urban design to solve complex design problems. The objective of this seminar to provide a deep understanding of the application of evolutionary algorithms in design – a process that is widely known to be a black box. This seminar tends to open this black box via the deep dive into the genetics of urban design problems and formulating data driven computational methodologies to solve urban problems with heirarchy of objectives, although challenging; once mastered, the application of evolutionary algorithms in design becomes a robust tool in addressing design problems comprised of multiple conflicting objectives that hold no clear single design solution.
Evolutionary strategies have been utilised widely since the late 20th century as robust problem-solving methods. The work of Sewell Wright in the 1930s is the earliest instance of the application of evolutionary principles as optimisation processes (Wright, 1932). Midway through the 20th century, John Holland’s genetic algorithms (GA) (Holland, 1962), Rechenberg and Schwefel’s evolutionary strategies (ES) (Rechenberg, 1965) and Fogel et al.’s evolutionary programming (EP) (Fogel et al., 1966) were developed independently from one another and led to the establishment of a unified field of Evolutionary Computation in the late 20th century as a result of several conferences in this field (De Jong, 2006).
Erns Myer described the evolutionary model as a two-step process; random variation within the genome of a phenotype and subsequently the selection of the phenotype through environmental pressures (Mayr, 1988). In the biological sciences, genotype or genome comprises a set of genes or instructions (codes) that performs as a blueprint for developing and constructing the phenotype. Phenotype is the physical expression (morphological and behavioural manifestation) of the genotype. In the field of evolutionary computation and in the context of design disciplines, a genotype is equivalent to a set of instructions or codes that will produce the geometry, namely the phenotype. In line with evolutionary processes in nature, the application of evolutionary computation in design is founded upon the two primary components of variation in the code responsible for generating the geometry (genotype) and the selection of the geometry (phenotype) that fits better in the environmental conditions. In this context, the environment is equivalent to a set of fitness objectives and the design constraints to be met. The successful construction of the genotype and phenotype to address the fitness objectives is crucial for the effective implementation of evolutionary principles as a design model.
Most of the widely used evolutionary algorithms, such as NSGA-II (developed by (Deb et al., 2000)) has been developed based on Mayr’s definition. The algorithm goes through a primary loop and starts with generating an initial random population of solutions. It continues with modifications of genomes through random variations and evaluation of the solutions on their objective performances. It ends with selecting a group of solutions based on a predefined selection mechanism (Fogel, 2008). Through this iterative process of generation, evaluation and selection, each phenotype (geometry or a design option) will be evaluated based on a set of objective measurements, and iteratively the population increases its fitness. Therefore, the formulation of the environment, which comprises the calculation of the fitness objectives, and the algorithmic construction of the genotype and phenotype (which are equal to the design problem) in this process is essential for constructing a successful evolutionary model to produce valid design options.
The term ‘Optimisation’ in evolutionary computation refers to finding the fittest solution (or best solution) to a problem that is constrained by a set of predefined limitations. In single-objective optimisation, where the problem comprises only one objective, finding the optimal solution is a relatively straight forward process. However, when a problem consists of two or more conflicting objectives (multiple objectives), finding the optimal solution becomes significantly more challenging; as a solution increases in fitness to one objective, it necessitates a decrease in fitness for the opposing objective; therefore, no single best solution that is optimal for both objectives is possible. As such, by employing an evolutionary approach to generate a set of optimal solutions, one can incrementally evolve a population of individuals that respond to the multiple conflicting objectives over multiple generations, thus limiting any user-influenced preferences throughout the simulation. Urban design problems are inherently complex and are required to address multiple set of objectives that are conflicting with one another, such as increase in density while increasing the solar gain. Thus, the application of multi-objective evolutionary algorithms in urban
design problems can provide deep and objective insight into the problem at hand and allow the urban designers to make an informed decision at every stage of the problem-solving process.
The seminar focuses on the development of 4 group projects. Students will be divided into 3 groups of three and 1 group of four. Each group will choose an urban tissue (from the provided urban tissues below), to which they must perform a thorough analysis of the tissue’s morphological properties and its environmental context. The groups must perform analysis on the urban tissue aligned with the data they gathered from their design studio project. This seminar explores and investigates computational workflows to abstract complex urban parameters into a series of concise yet meaningful quantifiable objectives with heirarchy of importance. Students will learn how to prioritise objectives in the context of complex urban design projects. This exercise forms a foundation to apply tools and techniques students learn in Generative Urban Design seminar in the context of their design studio project.
The project’s goal is to evolve variations of the primitive urban tissues that are adapted to the region’s environmental conditions and the selected set of urban objectives. Each group must define a minimum of 4 primary objectives and 5 secondary objectives comprised of environmental and urban performance related goals such as density, connectivity, accessibility, continuity, openness, etc. The urban performance-related objectives should be aligned with urban indicators and parameters that are being investigated in the parallel design studio. Each of these objectives will be prioritised and categorised into primary and secondary goals. Primary objectives will be incorporated as fitness criteria based on which the evolutionary engine will evolve urban tissues that are optimised for. Secondary objectives will be incorporated as Data input into the system based on which the users create a data driven selection strategies to further assist their decision making processes. The generated urban tissues should inherit the morphological characteristics of the chosen case studies. Rhino, Grasshopper, custom scripting component C# and the evolutionary engine Wallacei are the primary design tools incorporated in this seminar; however, students can use other plugins and methods to formulate their design problems.
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