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Difficulty of Unimodal and Multimodal Landscapes in Genetic Programming

Abstract : This paper presents an original study of fitness distance correlation as a measure of problem difficulty in genetic programming. A new definition of distance, called structural distance, is used and suitable mutation operators for the program space are defined. The difficulty is studied for a number of problems, including, for the first time in GP, multimodal ones, both for the new hand-tailored mutation operators and standard crossover. Results are in agreement with empirical observations, thus confirming that fitness distance correlation can be considered a reasonable index of difficulty for genetic programming, at least for the set of problems studied here.
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https://hal.archives-ouvertes.fr/hal-00159827
Contributor : Manuel Clergue <>
Submitted on : Wednesday, July 4, 2007 - 11:18:40 AM
Last modification on : Monday, October 12, 2020 - 10:30:28 AM
Long-term archiving on: : Thursday, April 8, 2010 - 10:31:21 PM

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Leonardo Vanneschi, Marco Tomassini, Manuel Clergue, Philippe Collard. Difficulty of Unimodal and Multimodal Landscapes in Genetic Programming. Genetic and Evolutionary Computation Conference - GECCO 2003, 2003, Chicago, United States. pp.1788-1799. ⟨hal-00159827⟩

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