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Using a simulated Wolbachia infection mechanism to improve multi-objective evolutionary algorithms

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Abstract

This paper presents a new evolutionary algorithm for solving multi-objective optimization problems. The proposed algorithm simulates the infection of the endosymbiotic bacteria Wolbachia to improve the evolutionary search. We conducted a series of computational experiments to contrast the results of the proposed algorithm to those obtained by state of the art multi-objective evolutionary algorithms (MOEAs). We employed two widely used test problem benchmarks. Our experimental results show that the proposed model outperforms established MOEAs at solving most of the test problems.

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Authors and Affiliations

  1. Carretera a Lago de Guadalupe Km 3.5, Col. Margarita Maza, Atizapan de Zaragoza, Estado de Mexico, Mexico

    Mauricio Guevara-Souza & Edgar E. Vallejo

  2. Computer Science Dept., ITESM Campus, Adolfo Lopez Mateos, Estado de Mexico, Mexico

    Mauricio Guevara-Souza & Edgar E. Vallejo

Authors
  1. Mauricio Guevara-Souza
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  2. Edgar E. Vallejo
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Correspondence to Mauricio Guevara-Souza.

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Guevara-Souza, M., Vallejo, E.E. Using a simulated Wolbachia infection mechanism to improve multi-objective evolutionary algorithms. Nat Comput 14, 157–167 (2015). https://doi.org/10.1007/s11047-013-9404-7

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  • DOI: https://doi.org/10.1007/s11047-013-9404-7

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