Abstract
This paper has the purpose to show game theory (GT) applied to genetic algorithms (GA) as a new type of interaction between individuals of GA. The game theory increases the exploration potential of the genetic algorithm by changing the fitness with social interaction between individuals, avoiding the algorithm to fall in a local optimum. To increase the exploitation potential of this approach, this work will present the adjusted crossover operator and compare results to other crossover methods.
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Yasojima, E.K.K., de Oliveira, R.C.L., Teixeira, O.N., Lisbôa, R., Mollinetti, M. (2016). Analyzing Genetic Algorithm with Game Theory and Adjusted Crossover Approach on Engineering Problems. In: Abraham, A., Han, S., Al-Sharhan, S., Liu, H. (eds) Hybrid Intelligent Systems. HIS 2016. Advances in Intelligent Systems and Computing, vol 420. Springer, Cham. https://doi.org/10.1007/978-3-319-27221-4_15
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DOI: https://doi.org/10.1007/978-3-319-27221-4_15
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