Wolbachia Infection Improves Genetic Algorithms as Optimization Procedure
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.
KeywordsWolbachia function optimization genetic algorithms
Unable to display preview. Download preview PDF.
- 4.Digalakis, J.G., Margaritis, K.G.: An experimental study of benchmarking functions for genetic algorithms. In: IEEE International Conference on Systems, Man and Cybernetics, pp. 3810–3815. IEEE Press, New York (2000)Google Scholar
- 11.Mitchell, M.: An Introduction to Genetic Algorithms. The MIT Press, Cambridge (1996)Google Scholar
- 12.Presgraves, D.C.: A genetic test of the mechanism of wolbachia-induced cytoplasmic incompatibility in drosophila. Genetics 154, 771–776 (2000)Google Scholar
- 13.Tang, K., Li, X., Suganthan, P.N., et al.: Benchmark functions for the cec 2010 special session and competition on large scale global optimization. Technical report. Nature Inspired Computation and Applications Laboratory, USTC, China (2009)Google Scholar