Fitness distance correlation in genetic programming: A constructive counterexample

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@InProceedings{vanneschi:2003:fdcigpacc,

• author = "L. Vanneschi and M. Tomassini and P. Collard and M. Clergue",
• title = "Fitness distance correlation in genetic programming: A constructive counterexample",
• booktitle = "Proceedings of the 2003 Congress on Evolutionary Computation CEC2003",
• editor = "Ruhul Sarker and Robert Reynolds and Hussein Abbass and Kay Chen Tan and Bob McKay and Daryl Essam and Tom Gedeon",
• pages = "289--296",
• year = "2003",
• publisher = "IEEE Press",
• address = "Canberra",
• publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA",
• month = "8-12 " # dec,
• organisation = "IEEE Neural Network Council (NNC), Engineers Australia (IEAust), Evolutionary Programming Society (EPS), Institution of Electrical Engineers (IEE)",
• keywords = "genetic algorithms, genetic programming, Algorithm design and analysis, Genetic mutations, Hamming distance, Laboratories, Sampling methods, Statistics, Stochastic processes, Tree data structures, statistical analysis, constructive counterexample, fitness distance correlation coefficient, hand-tailored function, infallible measure, problem difficulty",
• ISBN = "0-7803-7804-0",
• DOI = "doi:10.1109/CEC.2003.1299587",
• abstract = "The fitness distance correlation coefficient has been shown to be a reasonable measure to quantify problem difficulty in genetic algorithms and genetic programming for a wide set of problems. In this paper we present an hand-tailored function for which fitness distance correlation fails to correctly predict problem difficulty in genetic programming. This counterexample proves that fitness distance correlation, although reliable, is not an infallible measure to quantify problem difficulty.",
• notes = "CEC 2003 - A joint meeting of the IEEE, the IEAust, the EPS, and the IEE.",

}

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

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