IDEAS home Printed from https://ideas.repec.org/p/wop/safiwp/95-05-048.html
   My bibliography  Save this paper

Evolutionary Algorithms, Fitness Landscapes and Search

Author

Listed:
  • Terry Jones

Abstract

A new model of fitness landscapes suitable for the consideration of evolutionary and other search algorithms is developed and its consequences are investigated. Answers to the questions "What is a landscape?" "Are landscapes useful?" and "What makes a landscape difficult to search?" are provided. The model makes it possible to construct landscapes for algorithms that employ multiple operators, including operators that act on or produce multiple individuals. It also incorporates operator transition probabilities. The consequences of adopting the model include a "one operator, one landscape" view of algorithms that search with multiple operators. An investigation into crossover landscapes and hillclimbing algorithms on them illustrates the dual role played by crossover in genetic algorithms. This leads to the "headless chicken" test for the usefulness of crossover to a given genetic algorithm and to serious questions about the usefulness of maintaining a population. A "reverse hillclimbing" algorithms is presented that allows the determination of details of the basin of attraction points on a landscape. These details can be used to directly compare members of a class of hillclimbing algorithms and to accurately predict how long a particular hillclimber will take to discover a given point. A connection between evolutionary algorithms and the heuristic search algorithms of Artificial Intelligence and Operations Research is established. One aspect of this correspondence is investigated in detail: the relationship between fitness functions and heuristic functions. By considering how closely fitness functions approximate the ideal heuristic functions, a measure of search difficult is obtained. The measure, fitness distance correlation, is a remarkably reliableble indicator of problem difficulty for a genetic algorithm on many problems taken from the genetic algorithms literature, even though the measure incorporates no knowledge of the operation of a genetic algorithm. This leads to one answer to the question "What makes a problem hard (or easy) for a genetic algorithm?" The answer is perfectly in keeping with what has been well known in Artificial Intelligence for over thirty years.

Suggested Citation

  • Terry Jones, 1995. "Evolutionary Algorithms, Fitness Landscapes and Search," Working Papers 95-05-048, Santa Fe Institute.
    • Handle: RePEc:wop:safiwp:95-05-048
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Christian M. Reidys & Peter F. Stadler, 1998. "Neutrality in Fitness Landscapes," Working Papers 98-10-089, Santa Fe Institute.
    2. Koen Frenken & Luigi Marengo & Marco Valente, 1999. "Interdependencies, nearly-decomposability and adaption," CEEL Working Papers 9903, Cognitive and Experimental Economics Laboratory, Department of Economics, University of Trento, Italia.
    3. Terry Jones & Stephanie Forrest, 1995. "Genetic Algorithms and Heuristic Search," Working Papers 95-02-021, Santa Fe Institute.
    4. Peter F. Stadler & Rudi Seitz & Günter P. Wagner, 1999. "Evolvability of Complex Characters," Working Papers 99-01-001, Santa Fe Institute.
    5. Terry Jones, 1995. "One Operator, One Landscape," Working Papers 95-02-025, Santa Fe Institute.
    6. Ellips Masehian & Hossein Akbaripour & Nasrin Mohabbati-Kalejahi, 2013. "Landscape analysis and efficient metaheuristics for solving the n-queens problem," Computational Optimization and Applications, Springer, vol. 56(3), pages 735-764, December.
    7. Bill McKelvey, 1999. "Avoiding Complexity Catastrophe in Coevolutionary Pockets: Strategies for Rugged Landscapes," Organization Science, INFORMS, vol. 10(3), pages 294-321, June.
    8. Gunter P. Wagner & Peter F. Stadler, 1997. "Complex Adaptations and the Structure of Recombination Spaces," Working Papers 97-03-029, Santa Fe Institute.
    9. Peter F. Stadler & Robert Happel, 1995. "Random Field Models for Fitness Landscapes," Working Papers 95-07-069, Santa Fe Institute.
    10. C R Reeves & A V Eremeev, 2004. "Statistical analysis of local search landscapes," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 55(7), pages 687-693, July.
    11. Wim Hordijk, 1996. "The Structurte of the Synchronizing-CA Landscape," Working Papers 96-09-077, Santa Fe Institute.
    12. David Kane, 1996. "Local Hillclimbing on an Economic Landscape," Working Papers 96-08-065, Santa Fe Institute.
    13. Wim Hordijk, 1995. "A Measure of Landscapes," Working Papers 95-05-049, Santa Fe Institute.
    14. Peter F. Stadler & Gunjter P. Wagner, 1996. "The Algebraic Theory of Recombination Spaces," Working Papers 96-07-046, Santa Fe Institute.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:wop:safiwp:95-05-048. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Thomas Krichel (email available below). General contact details of provider: https://edirc.repec.org/data/epstfus.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.