Abstract
This paper introduces 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 experiments to compare the results of the proposed algorithm to those obtained by state of the art multi-objective evolutionary algorithms (MOEAs) at solving the ZDT test suite. Our experimental results show that the proposed model outperforms established MOEAs at solving most of the test problems.
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Guevara-Souza, M., Vallejo, E.E. (2013). WIGA: Wolbachia Infection Genetic Algorithm for Solving Multi-Objective Optimization Problems. In: Castro, F., Gelbukh, A., González, M. (eds) Advances in Soft Computing and Its Applications. MICAI 2013. Lecture Notes in Computer Science(), vol 8266. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-45111-9_4
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DOI: https://doi.org/10.1007/978-3-642-45111-9_4
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