Abstract
This paper shows how the addition of Wolbachia infection can improve evolutionary function optimization by preventing the system from sticking at local optima. Firstly a variant of genetic algorithms that allows the introduction of Wolbachia is described. Then an application of this system to the optimization of a collection of mutimodal functions is described. Finally, we show how the introduction of Wolbachia infection improves the procedure in terms of both fitness and the number of generations required to obtain the solutions.
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Guevara-Souza, M., Vallejo, E.E. (2012). Wolbachia Infection Improves Genetic Algorithms as Optimization Procedure. In: Dediu, AH., Martín-Vide, C., Truthe, B. (eds) Theory and Practice of Natural Computing. TPNC 2012. Lecture Notes in Computer Science, vol 7505. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33860-1_14
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DOI: https://doi.org/10.1007/978-3-642-33860-1_14
Publisher Name: Springer, Berlin, Heidelberg
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