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A Review of Applications of Evolutionary Algorithms in Pattern Recognition

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Pattern Recognition, Machine Intelligence and Biometrics

Abstract

This chapter presents a review of some of the most representative work regarding techniques and applications of evolutionary algorithms in pattern recognition. Evolutionary algorithms are a set of metaheuristics inspired on Darwins “survival of the fittest” principle which are stochastic in nature. Evolutionary algorithms present several advantages over traditional search and classification techniques, since they require less domain-specific information, are easy to use and operate on a set of solutions (the so-called population). Such advantages have made them very popular within pattern recognition (as well as in other domains) as will be seen in the review of applications presented in this chapter.

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Authors and Affiliations

  1. CINVESTAV-IPN (Evolutionary Computation Group) Departamento de Computación, Av. IPN No. 2508, Col. San Pedro Zacatenco, Mexico, D.F. 07360, Mexico

    Luis Gerardo de la Fraga

  2. UMI LAFMIA 3175 CNRS at CINVESTAV-IPN, Mexico

    Carlos A. Coello Coello

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  1. Luis Gerardo de la Fraga
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  1. CCIS, Northeastern University, 360 Huntington Ave, Boston, MA, 02110, USA

    Patrick S. P. Wang

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de la Fraga, L.G., Coello Coello, C.A. (2011). A Review of Applications of Evolutionary Algorithms in Pattern Recognition. In: Wang, P.S.P. (eds) Pattern Recognition, Machine Intelligence and Biometrics. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22407-2_1

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