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

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@Article{tomassini:2005:EC,

• author = "Marco Tomassini and Leonardo Vanneschi and Philippe Collard and Manuel Clergue",
• title = "A Study of Fitness Distance Correlation as a Difficulty Measure in Genetic Programming",
• journal = "Evolutionary Computation",
• year = "2005",
• volume = "13",
• number = "2",
• pages = "213--239",
• month = "Summer",
• keywords = "genetic algorithms, genetic programming, problem difficulty, program landscapes, fitness distance correlation",
• ISSN = "1063-6560",
• DOI = "doi:10.1162/1063656054088549",
• size = "27 pages",
• 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.",
• publisher = "MIT Press",
• notes = "http://mitpress.mit.edu/catalog/item/default.asp?ttype=4&tid=25",

}

Genetic Programming entries for Marco Tomassini Leonardo Vanneschi Philippe Collard Manuel Clergue

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