Abstract
While successful applications have been reported using standard GP crossover, limitations of this approach have been identified by several investigators. Among the most compelling alternatives to standard GP crossover are those that use some form of homologous crossover, where code segments that are exchanged are structurally or syntactically aligned in order to preserve context and worth. This paper reports the results of an empirical comparison of GP using standard crossover methods with GP using homologous crossover methods. Ten problems are tested, five each of pattern recognition and regression.
Results suggest that in terms of generalization accuracy, homologous crossover does generate consistently better performance. In addition, there is a consistently lower fraction of introns that are generated in the solution code.
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Hansen, J.V. Genetic Programming Experiments with Standard and Homologous Crossover Methods. Genetic Programming and Evolvable Machines 4, 53–66 (2003). https://doi.org/10.1023/A:1021825110329
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DOI: https://doi.org/10.1023/A:1021825110329