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Recent Advances in the Theory and Application of Fitness Landscapes pp 133–152Cite as

Fitness Landscapes and Problem Difficulty in Evolutionary Algorithms: From Theory to Applications

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Part of the book series: Emergence, Complexity and Computation ((ECC,volume 6))

Abstract

Above many successes of evolutionary algorithms in solving computationally hard optimisations problems, a major challenge in practice remains how to select/construct the best suited algorithm when solving a problem. The well-known no free lunch theorem rules out the possibility of developing one best algorithmgenerally suitable for solving all problems. Within the realm of algorithm selection in general, the problem becomes how can we characterise problem hardness with reference to evolutionary algorithms (EAs). For the first time, this chapter rigorously derives a problem hardness measure from a theoretical difficulty measure widely used in complexity theory of EAs. Furthermore, the proposed measure is applied to construct an offline optimisation algorithm and an online optimisation algorithm. On one hand, the measure is incorporated with a machine learning algorithm for parameter tuning and achieves powerful performance. On the other hand, an adaptive algorithm framework is proposed and shows promising results. We argue that the proposed measure is general, yet powerful as an indicator of EA-hardness, and contribute to the goal of constructing better suited algorithms for solving problems.

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Author information

Authors and Affiliations

  1. Joint USTC-Birmingham Research Institute in Intelligent Computation and Its Applications The Centre of Excellence for Research in Computational Intelligence and Applications (CERCIA), School of Computer Science, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK

    Guanzhou Lu & Xin Yao

  2. Joint USTC-Birmingham Research Institute in Intelligent Computation and Its Applications The Nature Inspired Computation and Applications Laboratory (NICAL), School of Computer Science and Technology, University of Science and Technology of China, Hefei, 230027, China

    Jinlong Li

Authors
  1. Guanzhou Lu
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  2. Jinlong Li
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  3. Xin Yao
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Correspondence to Guanzhou Lu .

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Editors and Affiliations

  1. Faculty of Electrical Engineering & Information Technology, HTWK Leipzig University of Applied Sciences, Leipzig, Germany

    Hendrik Richter

  2. Department of Computer Science, University of Pretoria, Pretoria, South Africa

    Andries Engelbrecht

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Lu, G., Li, J., Yao, X. (2014). Fitness Landscapes and Problem Difficulty in Evolutionary Algorithms: From Theory to Applications. In: Richter, H., Engelbrecht, A. (eds) Recent Advances in the Theory and Application of Fitness Landscapes. Emergence, Complexity and Computation, vol 6. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41888-4_5

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  • DOI: https://doi.org/10.1007/978-3-642-41888-4_5

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-41887-7

  • Online ISBN: 978-3-642-41888-4

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