WIT Press

Simulating Evolution With Mathematica

Price

Free (open access)

Paper DOI

10.2495/IMS970351

Pages

10

Published

1997

Size

1,119 kb

Author(s)

C. Jacob

Abstract

Evolutionary mechanisms as observed in nature are successfully used in evo- lutionary algorithms (EA) in order to solve complex optimization tasks or to mimick 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 dis- cussed: 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 evolu- tion of robot control programs shows a simple GP-application. The article concludes with a more sophisticated GP-example: the breeding o

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