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Performance Classification of Genetic Algorithms on Continuous Optimization Problems

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8857))

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Abstract

Modelling the behaviour of algorithms is the realm of Evolutionary Algorithm theory. From a practitioner’s point of view, theory must provide some guidelines regarding which algorithm/parameters to use in order to solve a particular problem. Unfortunately, most theoretical models of evolutionary algorithms are difficult to apply to realistic situations. Recently, there have been works that addressed this problem by proposing models of performance of different Genetic Programming Systems. In this work, we complement previous approaches by proposing a scheme capable of classifying the hardness of optimization problems based on different difficulty measures such as Negative Slope Coefficient, Fitness Distance Correlation, Neutrality, Ruggedness, Basins of Attraction, and Epistasis. The results indicate that this procedure is able to accurately classify the performance of the GA over a set of benchmark problems.

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Authors and Affiliations

  1. Universidad Michoacana de San Nicolas de Hidalgo, Av. Francisco J. Mugica S/N Ciudad Universitaria, Morelia, Michoacan, Mexico

    Noel E. Rodriguez-Maya, Mario Graff & Juan J. Flores

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  1. Noel E. Rodriguez-Maya
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  2. Mario Graff
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  3. Juan J. Flores
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Editor information

Editors and Affiliations

  1. Centro de Investigación en Computación, Instituto Politécnico Nacional, Av. Juan Dios Bátiz s/n, Col. Nueva Industrial Vallejo, 07738, Mexico City, Mexico

    Alexander Gelbukh

  2. Universidad Autónoma del Estado de Hidalgo, Área Académica de Computación y Electrónica, Carretera Pachuca-Tulancingo, , Km. 4.5, Col. Carboneras, Mineral de la Reforma,, 42180, Hidalgo, Mexico

    Félix Castro Espinoza

  3. Facultad de ciencias, Universidad Autónoma Nacional de México, Ciudad Universitaria, México DF, Mexico

    Sofía N. Galicia-Haro

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Rodriguez-Maya, N.E., Graff, M., Flores, J.J. (2014). Performance Classification of Genetic Algorithms on Continuous Optimization Problems. In: Gelbukh, A., Espinoza, F.C., Galicia-Haro, S.N. (eds) Nature-Inspired Computation and Machine Learning. MICAI 2014. Lecture Notes in Computer Science(), vol 8857. Springer, Cham. https://doi.org/10.1007/978-3-319-13650-9_1

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  • DOI: https://doi.org/10.1007/978-3-319-13650-9_1

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-13649-3

  • Online ISBN: 978-3-319-13650-9

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