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Crossover operators to control size growth in linear GP and variable length GAs

By Dominique Chu


In various nuances of evolutionary algorithms it has been observed that variable sized genomes exhibit large degrees of redundancy and corresponding undue growth. This phenomenon is commonly referred to as bloat. The present contribution investigates the role of crossover operators as the cause for length changes in variable length genetic algorithms and linear GP. Three crossover operators are defined; each is tested with three different fitness functions. The aim of this article is to indicate suitable designs of crossover operators that allow efficient exploration of designs of solutions of a wide variety of sizes, while at the same time avoiding bloat

Topics: QA76
Publisher: IEEE
Year: 2008
OAI identifier:

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  7. (1997). Fitness causes bloat. In doi
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