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Fitness Landscape Analysis for Metaheuristic Performance Prediction

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Recent Advances in the Theory and Application of Fitness Landscapes

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

Abstract

Metaheuristics have become popular for solving complex optimisation problems where classical techniques are either infeasible or perform poorly. Despite many success stories, it is well known that metaheuristics sometimes fail and that researchers and practitioners frequently resort to trial and error to find an appropriate algorithm or setting to solve a given problem. Within the framework of the general algorithm selection problem, this chapter addresses the feasibility of predicting algorithm performance on unknown real-valued problems based on fitness landscape features. Normalized metrics are proposed for quantifying algorithm performance on known problems to generate suitable training data. Performance metrics are tested using a standard particle swarm optimisation algorithm and are investigated alongside three existing fitness landscape measures. This preliminary investigation highlights the need for a shift in focus away from predicting general problem hardness towards characterising problems where each fitness landscape technique has value as a part-predictor of algorithm performance.

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  1. Department of Computer Science, University of Pretoria, Pretoria, 0002, South Africa

    Katherine M. Malan & Andries P. Engelbrecht

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  1. Katherine M. Malan
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  2. Andries P. Engelbrecht
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Correspondence to Katherine M. Malan .

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  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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Malan, K.M., Engelbrecht, A.P. (2014). Fitness Landscape Analysis for Metaheuristic Performance Prediction. 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_4

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  • DOI: https://doi.org/10.1007/978-3-642-41888-4_4

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