Evaluating alternative forms of crossover in evolutionary computation on linear systems of equations

David B. Fogel; Peter J. Angeline

doi:10.1117/12.326732

13 October 1998 Evaluating alternative forms of crossover in evolutionary computation on linear systems of equations

David B. Fogel, Peter J. Angeline

Proceedings Volume 3455, Applications and Science of Neural Networks, Fuzzy Systems, and Evolutionary Computation; (1998) https://doi.org/10.1117/12.326732
Event: SPIE's International Symposium on Optical Science, Engineering, and Instrumentation, 1998, San Diego, CA, United States

Abstract

Experiments are conducted to assess the utility of alternative crossover operators within a framework of evolutionary computation. Systems of linear equations are used for testing the efficiency of one-point, two-point, and uniform crossover. The results indicate that uniform crossover, which disrupts building blocks maximally, generates statistically significantly better solutions than one- or two-point crossover. Moreover, for the cases of small population sizes, crossing over existing solutions with completely random solutions can perform as well or better than the traditional one- and two-point operators.

Citation Download Citation

David B. Fogel and Peter J. Angeline "Evaluating alternative forms of crossover in evolutionary computation on linear systems of equations", Proc. SPIE 3455, Applications and Science of Neural Networks, Fuzzy Systems, and Evolutionary Computation, (13 October 1998); https://doi.org/10.1117/12.326732

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