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A Review of Major Application Areas of Differential Evolution

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Book cover Advances in Differential Evolution

Part of the book series: Studies in Computational Intelligence ((SCI,volume 143))

Summary

In this chapter we present an overview of the major applications areas of differential evolution. In particular we pronounce the strengths of DE algorithms in tackling many difficult problems from diverse scientific areas, including single and multiobjective function optimization, neural network training, clustering, and real life DNA microarray classification. To improve the speed and performance of the algorithm we employ distributed computing architectures and demonstrate how parallel, multi–population DE architectures can be utilised in single and multiobjective optimization. Using data mining we present a methodology that allows the simultaneous discovery of multiple local and global minimizers of an objective function. At a next step we present applications of DE in real life problems including the training of integer weight neural networks and the selection of genes of DNA microarrays in order to boost predictive accuracy of classification models. The chapter concludes with a discussion on promising future extensions of the algorithm, and presents novel mutation operators, that are the result of a genetic programming procedure, as very interesting future research direction.

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Authors and Affiliations

  1. Computational Intelligence Laboratory, Department of Mathematics,University of Patras Artificial Intelligence Research Center–UPAIRC, University of Patras, GR–26110, Patras, Greece

    V. P. Plagianakos & M. N. Vrahatis

  2. Institute for Mathematical Sciences, Imperial College London,South Kensington, London, SW7 2PG, United Kingdom

    D. K. Tasoulis

Authors
  1. V. P. Plagianakos
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  2. D. K. Tasoulis
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  3. M. N. Vrahatis
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Uday K. Chakraborty

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Plagianakos, V.P., Tasoulis, D.K., Vrahatis, M.N. (2008). A Review of Major Application Areas of Differential Evolution. In: Chakraborty, U.K. (eds) Advances in Differential Evolution. Studies in Computational Intelligence, vol 143. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-68830-3_8

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  • DOI: https://doi.org/10.1007/978-3-540-68830-3_8

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-68827-3

  • Online ISBN: 978-3-540-68830-3

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