A Study of Fitness Distance Correlation as Difficulty Measure in Genetic Programming

Marco Tomassini 1 Leonardo Vanneschi 1 Philippe Collard 2 Manuel Clergue 2
1 Information Systems Department
2 I3S - Laboratoire d'Informatique, Signaux, et Systèmes de Sophia Antipolis
Abstract : We present an approach to genetic programming difficulty based on a statistical study of program fitness landscapes. The fitness distance correlation is used as an indicator of problem hardness and we empirically show that such a statistic is adequate in nearly all cases studied here. However, fitness distance correlation has some known problems and these are investigated by constructing an artificial landscape for which the correlation gives contradictory indications. Although our results confirm the usefulness of fitness distance correlation, we point out its shortcomings and give some hints for improvement in assessing problem hardness in genetic programming.
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Article dans une revue
Evolutionary Computation, Massachusetts Institute of Technology Press (MIT Press), 2005, 13 (2), pp.213-239. 〈10.1162/1063656054088549〉
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https://hal.archives-ouvertes.fr/hal-00159336
Contributeur : Manuel Clergue <>
Soumis le : mardi 3 juillet 2007 - 02:50:21
Dernière modification le : mercredi 31 janvier 2018 - 10:24:04

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Marco Tomassini, Leonardo Vanneschi, Philippe Collard, Manuel Clergue. A Study of Fitness Distance Correlation as Difficulty Measure in Genetic Programming. Evolutionary Computation, Massachusetts Institute of Technology Press (MIT Press), 2005, 13 (2), pp.213-239. 〈10.1162/1063656054088549〉. 〈hal-00159336〉

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