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A Multi-Objective Relative Clustering Genetic Algorithm with Adaptive Local/Global Search based on Genetic Relatedness

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Applications of Evolutionary Computation (EvoApplications 2014)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 8602))

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Abstract

This paper describes a new evolutionary algorithm for multi-objective optimization, namely Multi-Objective Relative Clustering Genetic Algorithm (MO-RCGA), inspired by concepts borrowed from gene relatedness and kin selection theory. The proposed algorithm clusters the population into different families based on individual kinship, and adaptively chooses suitable individuals for reproduction. The idea is to use the information on the position of the individuals in the search space provided by such clustering schema to enhance the convergence rate of the algorithm, as well as improve its exploration. The proposed algorithm is tested on ten unconstrained benchmark functions proposed for the special session and competition on multi-objective optimizers held at IEEE CEC 2009. The Inverted Generational Distance (IGD) is used to assess the performance of the proposed algorithm, in comparison with the IGD obtained by state-of-the-art algorithms on the same benchmark.

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Authors and Affiliations

  1. Department of Mechanical Engineering, University of Guilan, Guilan, Iran

    Iman Gholaminezhad

  2. INCAS3, Dr. Nassaulaan 9, 9401 HJ, Assen, The Netherlands

    Giovanni Iacca

Authors
  1. Iman Gholaminezhad
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  2. Giovanni Iacca
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Corresponding author

Correspondence to Giovanni Iacca .

Editor information

Editors and Affiliations

  1. Depto. Estadística e Investigación, Universidad Politécnica de Valencia, Valencia, Spain

    Anna I. Esparcia-Alcázar

  2. University of Granada, Granada, Spain

    Antonio M. Mora

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Gholaminezhad, I., Iacca, G. (2014). A Multi-Objective Relative Clustering Genetic Algorithm with Adaptive Local/Global Search based on Genetic Relatedness. In: Esparcia-Alcázar, A., Mora, A. (eds) Applications of Evolutionary Computation. EvoApplications 2014. Lecture Notes in Computer Science(), vol 8602. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45523-4_48

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  • DOI: https://doi.org/10.1007/978-3-662-45523-4_48

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-45522-7

  • Online ISBN: 978-3-662-45523-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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