Directing Crossover For Reduction Of Bloat In Gp
by
Terrio And Heywood
,
M. D. Terrio
,
M. I. Heywood
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@MISC{Heywood_directingcrossover,
author = {Terrio And Heywood and M. D. Terrio and M. I. Heywood},
title = {Directing Crossover For Reduction Of Bloat In Gp},
year = {}
}
author = {Terrio And Heywood and M. D. Terrio and M. I. Heywood},
title = {Directing Crossover For Reduction Of Bloat In Gp},
year = {}
}
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Abstract:
A method is proposed to reduce the amount of inviable code (or bloat) produced in individuals while searching for a parsimonious solution under tree structured genetic programming. Known as directed crossover, this process involves the identification of highly fit nodes to use as crossover points during operator application. Three test problems, including medical data classification, are used to assess the performance of directed crossover when applied at various thresholds. Results, collected over 1260 independent runs, identify conditions under which directed crossover reduces code bloat.
