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. © 2012 Springer-Verlag.