Real-Valued Genetic Algorithms with Disagreements
This paper introduces a new mutation operator for real-valued genetic algorithms that refines the evolutionary process using disagreements. After a short introduction, we describe the new concept theoretically and then we exemplify it by defining a Gaussian distribution-based disagreements operator: the 6σ-GAD. We transform two common real-valued genetic algorithms into their disagreements-enabled counterparts and we conduct several tests proving that our newly obtained algorithms perform better because they gain strengthened neighborhood focus using partial disagreements and enhanced exploration capabilities through extreme disagreements.
KeywordsGenetic Algorithm Particle Swarm Optimization Mutation Operator Partial Disagreement Extreme Disagreement
Unable to display preview. Download preview PDF.
- 1.Bäck, T., Fogel, D.B., Michalewicz, T.: Basic Algorithms and Operators. In: Evolutionary Computation, vol. 1. Institute of Physics Publishing, Bristol (1999)Google Scholar
- 2.Bergh, F.: An Analysis of Particle Swarm Optimizers (PhD thesis). University of Pretoria, Pretoria (2001)Google Scholar
- 3.Bremermann, H.J.: The evolution of intelligence. The nervous system as a model of its environment, Technical report, no.1, contract no. 477(17), Dept. Mathematics, Univ. Washington, Seattle (1958)Google Scholar
- 4.Darwin, C.H.: On the Origin of Species by Means of Natural Selection or the Preservation of Favoured Races in the Struggle for Life. Murray, London (1859)Google Scholar
- 7.EvA2 Project Homepage, http://www.ra.cs.uni-tuebingen.de/software/EvA2 (accessed in January 2011)
- 8.Fraser, A.: Simulation of genetic systems by automatic digital computers. I. Introduction. Australian Journal of Biological Sciences 10, 484–491 (1957)Google Scholar
- 11.Holland, J.: Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor (1975)Google Scholar
- 14.Lihu, A., Holban, Ş.: Particle Swarm Optimization with Disagreements on Stagnation. In: Semantic Methods for Knowledge Management and Communication - 3rd International Conference on Computational Collective Intelligence - Technologies and Applications, ICCCI 2011, Gdynia, Poland, September 21-23. SCI, vol. 7092. Springer, Heidelberg (2011)Google Scholar
- 15.Mühlenbein, H., Schlierkamp-Voosen, D.: Predictive Models for the Breeder Genetic Algorithm, I. Continuous Parameter Optimization. Evolutionary Computation 1(1), 25–49 (1993)Google Scholar