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http://dx.doi.org/10.9708/jksci.2011.16.3.045

Improvement of evolution speed of individuals through hybrid reproduction of monogenesis and gamogenesis in genetic algorithms  

Jung, Sung-Hoon (Dept. of Information and Communications Engineering, Hansung University)
Publication Information
Journal of the Korea Society of Computer and Information / v.16, no.3, 2011 , pp. 45-51 More about this Journal
Abstract
This paper proposes a method to accelerate the evolution speed of individuals through hybrid reproduction of monogenesis and gamogenesis. Monogenesis as a reproduction method that bacteria or monad without sexual distinction divide into two individuals has an advantage for local search and gamogenesis as a reproduction method that individuals with sexual distinction mate and breed the offsprings has an advantages for keeping the diversity of individuals. These properties can be properly used for improvement of evolution speed of individuals in genetic algorithms. In this paper, we made relatively good individuals among selected parents to do monogenesis for local search and forced relatively bad individuals among selected parents to do gamogenesis for global search by increasing the diversity of chromosomes. The mutation probability for monogenesis was set to a lower value than that of original genetic algorithm for local search and the mutation probability for gamogenesis was set to a higher value than that of original genetic algorithm for global search. Experimental results with four function optimization problems showed that the performances of three functions were very good, but the performances of fourth function with distributed global optima were not good. This was because distributed global optima prevented individuals from steady evolution.
Keywords
genetic algorithms; monogenesis; gamogenesis; evolution;
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  • Reference
1 Silja Meyer-Nieberg and Hans-Georg Beyer, "Self-Adaptation in Evolutionary Algorithms," Studies in Computational Intelligence, Vol. 54, pp. 47-75, 2007.   DOI
2 K. DeJong, "An Analysis of the Behavior of a Class of Genetic Adaptive Systems", Ph. D. Dissertation, University of Michigan, 1975.
3 Marcin Molga and Czeslaw Smutnicki, "Test functions for optimization needs," http: / /www.zsd.ict.pwr.wroc.pl/ files/ docs/ functions.pdf
4 D. Goldberg, "Genetic Algorithms in Search, Optimization and Machine Learning". Addison-Wesley, 1989.
5 J. Andre, P. Siarry, and T. Dognon, "An improvement of the standard genetic algorithm fighting premature convergence in continuous optimization," Advances in engineering software, Vol. 32, No. 1, pp. 49-60, 2001.   DOI   ScienceOn
6 M. Srinivas and L. M. Patnaik, "Adaptive Probabilities of Crossover and Mutation in Genetic Algorithm," IEEE Transactions on Systems, Man and Cybernetics, Vol. 24, pp. 656-667, Apr. 1994.   DOI   ScienceOn
7 A. Tuson, "Adapting Operator Probabilities in Genetic Algorithms," master thesis, Dept. of Artificial Intelligence, University of Edinburgh, UK, 1995.
8 E. Alba and B. Dorronsoro, "The exploration/exploitation tradeoff in dynamic cellular genetic algorithms," IEEE Transactions on Evolutionary Computation, Vol. 9, No. 2, pp. 126-142, 2005.   DOI   ScienceOn
9 A. E. Eiben, Z. Michalewicz, m. Schoenauer, and J. E. Smith, "Parameter Control in Evolutionary Algorithms", Studies in Computational Intelligence, Vol. 54, pp. 19-46, 2007.   DOI