Evolutionary Algorithms

Evolutionary Algorithms (EA) are heuristic search methods which have been applied in optimisation problems in a wide range of fields. They have been developed with the goal of presenting a population of optimised solutions instead of just one single point (Marler & Arora, 2004). EA are very robust methods and can handle all type of fitness parameters and variables (Andersson, 2001).

Research on the use of EA for optimisation in the field of design related fields goes back to the early 1970's when Rechenberg and Holland first published their work on this subject. Rechenberg and his research team applied the concept of Genetic Algorithms (GA) for the optimisation of complex engineering problems, and Holland studied adaption and complex adaptive processes providing support for the development of evolutionary algorithms (Holland, 1992).

Evolutionary Algorithms are based on the principals of natural selection (Deb, 2001). Each optimisation parameter is coded into a gene as a string of bits. All optimisation parameters together form a chromosome and describe an individual. Depending on each specific problem a chromosome could be an array of real numbers, a binary string, a list of components in a data base, etc. (Andersson, 2001). Each individual represents a solution and a set of

individuals form a population. Within one population the fittest are selected for combination. The combination of those genes results in a child. The

30

children are reinserted in the population and the procedure starts again. The optimisation continues until the population has converged or until the maximum number of generations has been reached. A lot of different kinds of EA have been developed, all with different features in order to solve a specific type of problems. Especially in the field of soft computing EA’s were refined and optimised with the objective to make them more useful for realistic applications. A first practical Pareto based approach to Multi Objective Evolutionary Algoritms (MOEAs) was developed and proposed by Goldberg in 1989 (Fonseca & Fleming, 1995) and this seminal work was the basis for further research in EA and their practical applications (Baeck, Fogel & Michalewicz, 1997).

Important for research in evolutionary algorithms for creative applications is the development of techniques to avoid the tendency to lose diversity within the population of feasible solutions and to converge into a single solution. Therefore the genetic algorithm is modified to function with multiple

objectives and applies niching pressure to spread a diverse population along a Pareto optimal trade off frontier or surface (Fonseca & Fleming, 1995) (Caldas, 2005). Those techniques are modelled after the idea of niching in the study of species in nature where natural evolutionary processes maintain a variety of species. Digital evolution research platforms, such as ‘Avida’, are available to the research community for benchmarking and referencing mathematical and computational applications and algorithms developed to understand the complexity of evolution and propose techniques to avoid the pitfalls of earlier EA’s which could not avoid the paths leading to evolutionary dead ends (Avida, 2011).

Thus, niching and other techniques are used to avoid that only one solution is located even when multiple solutions exist. This happens in traditional EA’s when individuals in a population become nearly identical too soon. A niching technique allows for EA’s to maintain a population of diverse individuals and are capable of locating multiple optimal solutions within a single population. The maintenance of diversity is important because diversity along the Pareto frontier helps in the search for new and improved trade-offs, which at the end are the ultimate goal of the use of optimisation in the design field.

31

For research in the field of architecture and design a MOEA developed and adapted by Gaspar-Cunha (2009) can be used (Fig. 10). This Reduced Pareto Set Genetic Algorithm (RPSGA) uses the technique of clustering to reduce the number of solutions on the Pareto front, thus contributing to a more efficient process of optimisation and making this particular kind of MOEA a possibly valuable part of interactive optimisation.

Fig. 10 – Flowchart of a MOEA (adapted from Fontes & Gaspar-Cunha, 2010.

In the field of architectural design, Caldas has done some extensive research in the use of genetic and evolutionary algorithms with the objective of

optimisation of multi-criteria problems, involving the improvement of environmental performance in building design. She introduces a Generative Design System as a method that incorporates evolutionary systems and adaptation paradigms in an architectural design process. Her Generative System is based on a Evolutionary Algorithms as search and optimisation engine.

32

She extends her research to multi-criteria problems using Pareto-based methods to evaluate the generated geometries for conflicting objectives. (Caldas, 2005; 2006) (Caldas and Norford, 2002).

She also studies and introduces the technique of niche induction in the application of GA’s in complex domains, compares different approaches and applies her conclusions to the testing of an existing building by Alvaro Siza. In her final conclusions she raises the question if the integration of all possible evaluation measures of a building in one single system, using a kind of

building DNA that would search for the optimal solution for all those evaluation criteria, can or will be desirable. She further concludes that the concept of an optimal solution as the ultimate goal does not make sense in a highly complex domain such as architecture. It might be better to get some insight and understanding in part of the process and leave some other decision-making to the personal interpretation of the designer or the

architect. Design intent, she affirms, cannot be excluded from an architectural design process, and design intent depends in part on the designer or the architect himself (Caldas, 2005).

In a different field genetic algorithms were also applied by Eckert (1999a) in the development and testing of a special purpose model for the automated design of knitwear. She argues that interactive generative systems can be powerful tools for human designers and that those systems naturally fit into human design thinking. Her research also indicates that generative tools increase the creativity and the productivity of human designers, and that those generative tools can be used in a variety of design tasks in an easy,

intuitive and effective way. However she points out to human bias as the main

factor which disturbs, and ultimately distorts the objectives of an optimisation process. Although one can argue that it is precisely this bias which makes optimisation acceptable as part of a design process.

Based on the same principles, Eckert and fellow researcher Ian Kelly (Eckert, Kelly, & Stacey, 1999b) have implemented several evolutionary systems to assist artists and designers in selecting colour combinations. Kelly’s aim was to

33

develop a generic tool that exploits the findings of colour science and helps the designer with the selection of colours.