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An Introduction to Fitness Landscape Analysis and Cost Models for Local Search

In: Handbook of Metaheuristics

Author

Listed:
  • Jean-Paul Watson

    (Sandia National Laboratories)

Abstract

Despite their empirical effectiveness, our theoretical understanding of metaheuristic algorithms based on local search (and all other paradigms) is very limited, leading to significant problems for both researchers and practitioners. Specifically, the lack of a theory of local search impedes the development of more effective metaheuristic algorithms, prevents practitioners from identifying the metaheuristic most appropriate for a given problem, and permits widespread conjecture and misinformation regarding the benefits and/or drawbacks of particular metaheuristics. Local search metaheuristic performance is closely linked to the structure of the fitness landscape, i.e., the nature of the underlying search space. Consequently, understanding such structure is a first step toward understanding local search behavior, which can ultimately lead to a more general theory of local search. In this chapter, we introduce and survey the literature on fitness landscape analysis for local search, placing the research in the context of a broader, critical classification scheme delineating methodologies by their potential to account for local search metaheuristic performance.

Suggested Citation

  • Jean-Paul Watson, 2010. "An Introduction to Fitness Landscape Analysis and Cost Models for Local Search," International Series in Operations Research & Management Science, in: Michel Gendreau & Jean-Yves Potvin (ed.), Handbook of Metaheuristics, chapter 0, pages 599-623, Springer.
    • Handle: RePEc:spr:isochp:978-1-4419-1665-5_20
    DOI: 10.1007/978-1-4419-1665-5_20
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    Citations

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    Cited by:

    1. Michael Schneider & Michael Drexl, 2017. "A survey of the standard location-routing problem," Annals of Operations Research, Springer, vol. 259(1), pages 389-414, December.
    2. Drexl, Michael & Schneider, Michael, 2015. "A survey of variants and extensions of the location-routing problem," European Journal of Operational Research, Elsevier, vol. 241(2), pages 283-308.
    3. Garza-Fabre, Mario & Toscano-Pulido, Gregorio & Rodriguez-Tello, Eduardo, 2015. "Multi-objectivization, fitness landscape transformation and search performance: A case of study on the hp model for protein structure prediction," European Journal of Operational Research, Elsevier, vol. 243(2), pages 405-422.
    4. Aparecida de Fátima Castello Rosa & Fabio Henrique Pereira, 2024. "An intensification approach based on fitness landscape characteristics for job shop scheduling problem," Journal of Combinatorial Optimization, Springer, vol. 47(5), pages 1-21, July.

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