A Study of Fitness Distance Correlation as a Difficulty Measure in Genetic Programming
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- @Article{tomassini:2005:EC,
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author = "Marco Tomassini and Leonardo Vanneschi and
Philippe Collard and Manuel Clergue",
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title = "A Study of Fitness Distance Correlation as a
Difficulty Measure in Genetic Programming",
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journal = "Evolutionary Computation",
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year = "2005",
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volume = "13",
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number = "2",
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pages = "213--239",
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month = "Summer",
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keywords = "genetic algorithms, genetic programming, problem
difficulty, program landscapes, fitness distance
correlation",
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ISSN = "1063-6560",
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DOI = "doi:10.1162/1063656054088549",
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size = "27 pages",
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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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publisher = "MIT Press",
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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
Citations