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Simulating Evolution with Mathematica
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| Organization: | University of Calgary |
| Department: | Computer Science |
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| Innovation in Mathematics: Proceedings of the Second International Mathematica Symposium |
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Evolutionary mechanisms as observed in nature are successfully used in evolutionary algorithms (EA) in order to solve complex optimization tasks or to mimic natural evolution processes. We present a collection of evolutionary algorithms which we have implemented in Mathematica together with some visualization examples and applications. The three major EA-classes are discussed: Evolution Strategies (ES), Genetic Algorithms (GA), and Genetic Programming (GP). Interactive evolution is demonstrated by the breeding of biomorphs, recursively branched line drawings. Multi-modal ES- and GA- experiments are demonstrated for a parameter optimization task. The evolution of robot control programs shows a simple GP-application. The article concludes with a more sophisticated GP-example: the breeding of developmental programs for artificial plantlike structures encoded on the basis of Lin-denmayer systems.
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