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Fitness Distance Correlation as a Measure of Problem Difficulty for Genetic Algorithms

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  • Terry Jones
  • Stephanie Forrest

Abstract

A measure of search difficulty, fitness distance correlation (FDC), is introduced and its power as a predictor of genetic algorithm (GA) performance is investigated. The sign and magnitude of this correlation can be used to predict the performance of a GA on many problems where the global maxima are already known. FDC can be used to correctly classify easy deceptive problems and easy and difficult non-deceptive problems as difficult, it can be used to indicate when Gray coding will prove better than binary coding, it produces the expected answers when applied to problems over a wide range of apparent difficulty, and it is also consistent with the surprises encountered when GAs were used on the Tanese and royal road functions. The FDC measure is a consequence of an investigation into the connection between GAs and heuristic search.

Suggested Citation

  • Terry Jones & Stephanie Forrest, 1995. "Fitness Distance Correlation as a Measure of Problem Difficulty for Genetic Algorithms," Working Papers 95-02-022, Santa Fe Institute.
    • Handle: RePEc:wop:safiwp:95-02-022
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    References listed on IDEAS

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    1. Terry Jones & Gregory J. E. Rawlins, 1993. "Reverse Hillclimbing, Genetic Algorithms and the Busy Beaver Problem," Working Papers 93-04-024, Santa Fe Institute.
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    Cited by:

    1. Terry Jones & Stephanie Forrest, 1995. "Genetic Algorithms and Heuristic Search," Working Papers 95-02-021, Santa Fe Institute.
    2. Terry Jones, 1995. "One Operator, One Landscape," Working Papers 95-02-025, Santa Fe Institute.
    3. Solnon, Christine, 2008. "Combining two pheromone structures for solving the car sequencing problem with Ant Colony Optimization," European Journal of Operational Research, Elsevier, vol. 191(3), pages 1043-1055, December.
    4. Mak, Brenda & Blanning, Robert & Ho, Susanna, 2006. "Genetic algorithms in logic tree decision modeling," European Journal of Operational Research, Elsevier, vol. 170(2), pages 597-612, April.
    5. Elmaghraby, Wedad J. & Larson, Nathan, 2012. "Explaining deviations from equilibrium in auctions with avoidable fixed costs," Games and Economic Behavior, Elsevier, vol. 76(1), pages 131-159.
    6. Larson, Nathan & Elmaghraby, Wedad, 2008. "Procurement auctions with avoidable fixed costs: an experimental approach," MPRA Paper 32163, University Library of Munich, Germany, revised 2011.
    7. Krokhmal, Pavlo A. & Pardalos, Panos M., 2009. "Random assignment problems," European Journal of Operational Research, Elsevier, vol. 194(1), pages 1-17, April.
    8. Jaszkiewicz, Andrzej & Kominek, Pawel, 2003. "Genetic local search with distance preserving recombination operator for a vehicle routing problem," European Journal of Operational Research, Elsevier, vol. 151(2), pages 352-364, December.
    9. Stutzle, Thomas, 2006. "Iterated local search for the quadratic assignment problem," European Journal of Operational Research, Elsevier, vol. 174(3), pages 1519-1539, November.
    10. Khouja, Moutaz & Michalewicz, Zgibniew & Wilmot, Michael, 1998. "The use of genetic algorithms to solve the economic lot size scheduling problem," European Journal of Operational Research, Elsevier, vol. 110(3), pages 509-524, November.
    11. Vidal, Thibaut & Crainic, Teodor Gabriel & Gendreau, Michel & Prins, Christian, 2013. "Heuristics for multi-attribute vehicle routing problems: A survey and synthesis," European Journal of Operational Research, Elsevier, vol. 231(1), pages 1-21.

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