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Dynamic Fitness Landscape Analysis

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Evolutionary Computation for Dynamic Optimization Problems

Part of the book series: Studies in Computational Intelligence ((SCI,volume 490))

Abstract

Solving optimization problems with time varying objective functions by methods of evolutionary computation can be grounded on the theoretical framework of dynamic fitness landscapes. In this chapter, we define such dynamic fitness landscapes and discuss their properties. To this end, analyzing tools for measuring topological and dynamical landscape properties are studied. Based on these landscape measures we obtain an approach for drawing conclusion regarding characteristic features of a given optimization problem. This may allow to address the question of how difficult the problem is for an evolutionary search, and what type of algorithm is most likely to solve it successfully. The methodology is illustrated using a well-known example, the moving peaks.

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Authors and Affiliations

  1. Department of Measurement Technology and Control Engineering, Faculty of Electrical Engineering and Information Technology, HTWK Leipzig University of Applied Sciences, D-04251, Leipzig, Germany

    Hendrik Richter

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  1. Hendrik Richter
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Correspondence to Hendrik Richter .

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  1. , School of Computer Science and Informati, De Montfort University, The Gateway, Leicester, LE1 9BH, United Kingdom

    Shengxiang Yang

  2. , School of Computer Science, University of Birmingham, Birmingham, B15 2TT, United Kingdom

    Xin Yao

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Richter, H. (2013). Dynamic Fitness Landscape Analysis. In: Yang, S., Yao, X. (eds) Evolutionary Computation for Dynamic Optimization Problems. Studies in Computational Intelligence, vol 490. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38416-5_11

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  • DOI: https://doi.org/10.1007/978-3-642-38416-5_11

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