Metaheuristics for Genetic Optimal Design

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J. B. Dumont, J. Lefèvre: Metaheuristics for Genetic Optimal Design. In: Generative Art 2004.



Genetic Algorithm has already proved itself on several optimization problems. Since a few years, this heuristics has also been used in the design field, providing innovative, efficient and original results. Our work intends to exploit the potential of creative evolutionary system to produce 2D drawings.

This approach, known as evolutionary design, comes from the work of the zoologist Richard Dawkins about evolution. Dawkins wanted to illustrate in concrete terms the Principles of Darwin. He imagined a genotype describing simple shapes and a genetic algorithm to make them evolve. Because sometimes these drawings look like real living beings, they were called biomorphs.

The other approaches in this field tend to be, for most of them, either too constrained, restraining the creative potential of the algorithm, or too free, producing random and anarchic creation. Our MetaGOD (Metaheuristics for Genetic Optimal Design) system intends to provide a balance, similar to Dawkins’ Biomorph, between order and chaos.

MetaGOD is based on a dynamic tree-like structure able to describe drawings, which can be evolved by a genetic algorithm, using other heuristics like simulated annealing and advanced genetic programming. Each data structure in the computer matches with a drawing. The algorithm explores the different possible creations and makes them evolved to both a structural point of view and concerning the numeric attributes. This evolution is orientated by the expectation of the human creator who assesses each drawing every generation. In addition to original process for genetic crossover and mutation adapted to vectorial graphics, our approach is also based on other heuristics like simulated annealing and advanced genetic programming.

Extended Abstract


Used References

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