Abstract
Standard Genetic Algorithms (SGAs) is modeled as a simple set of fixed size individuals and each individual has no gender. The idea is based on non-random mating and important role of religious in the organization of societies. Essential concepts of religions are commandments and religious customs, which influence the behavior of the individuals. This paper proposes the Intimate-Based Assortative Mating Genetic Algorithm (IAMGA) and explores the affect of including intimate-based assortative mating to improve the performance of genetic algorithms. The IAMGA combined gender-based, variable-size and intimate-based assortative mating feature. All mentioned benchmark instances were clearly better than the performance of a SGA.
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Goldberg, D.E.: Genetic algorithms in Search, Optimization and Machine Learning. Addison-Wesley (1989)
Holland, J.H.: Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor (1975)
de Castro, J.P., Posta, A., Bittencourt, G.: A Genetic Algorithm with Feminine Selection. In: Proceedings of the 16th Eureopean Conference on Artificial Intelligence, ECAI 2004, pp. 244–248 (2004)
Sanchez-Velazco, J., Bullinaria, J.A.: Sexual Selection with Competitive/Co-Operative Operators for Genetic Algorithms. In: IASTED International Conference on Neural Networks and Computational Intelligence, NCI (2003)
Ansótegui, C., Sellmann, M., Tierney, K.: A Gender-Based Genetic Algorithm for the Automatic Configuration of Algorithms. In: Gent, I.P. (ed.) CP 2009. LNCS, vol. 5732, pp. 142–157. Springer, Heidelberg (2009)
Savic, A., Tosic, D., Maric, M., Kratica, J.: Genetic Algorithm Approach for Solving the Task Assignment Problem. Serdica Journal of Computing 2(3), 267–276 (2008)
Mahfouz, S.Y., Toropov, V.V., Westbrook, R.K.: Modification, tuning and testing of a GA for structural optimization problems. In: 1st AMSO UK/ISSMO Conference on Engineering Design Optimization, pp. 271–278 (1999)
Vrajitoru, D.: Simulating gender separation with genetic algorithms. In: Proceedings of the Genetic and Evolutionary Compuatation Conference (GECCO 2002), pp. 634–641 (2002)
Lis, J., Eiben, A.E.: A Multi-Sexual Genetic Algorithm for Multiobjective Optimization. In: Proceedings of the 1996 International Conference on Evolutionary Computation, Nagoya, Japan, pp. 59–64. IEEE (1996)
Rejeb, J., AbuElhaija, M.: New gender genetic algorithm for solving graph partitioning problems. In: Proceedings of the 43rd IEEE Midwest Symposium on Circuits and Systems, vol. 1, pp. 444–446 (2000)
Allenson, R.: Genetic algorithms with gender for multi-function optimization. Technical Report EPCC-SS92-01, Edinburgh Parallel Computing Centre (1992)
Ratford, M., Tuson, A., Thompson, H.: Applying Sexual Selection as a Mechanism for Obtaining Multiple Distinct Solutions. Presented at Emerging Technologies 1997 (1997a)
Ratford, M., Tuson, A., Thompson, H.: The Single Chromosome’s Guide to Dating. In: Third International Conference on Artificial Neural Networks and Genetic Algorithms (1997b)
Ronald, E.: When Selection meets Seduction. In: The Sixth International Conference on Genetic Algorithms, pp. 167–173 (1995)
Song Goh, K., Lim, A., Rodrigues, B.: Sexual Selection for Genetic Algorithms. Artificial Intelligence Review 19(2), 123–152 (2003)
Garcia-Martinez, C., Lozano, M.: Hybrid Real-Coded Genetic Algorithms with Female and Male Differentiation. In: Congress on Evolutionary Computation, pp. 896–903 (2005)
Wagner, S., Affenzeller, M.: SexualGA: Gender-Specific Selection for Genetic Algorithms. In: The 9th World Multi-Conference on Systemics, Cybernetics and Informatics, pp. 76–81 (2005)
Tang, K.: Benchmark Functions for the CEC 2010 Special Session and Competition on Large-Scale Global Optimization: Nature Inspired Computation and Applications Laboratory (2010), http://nical.ustc.edu.cn/cec10ss.php Technical report
De Jong, K.A.: Analysis of the Behavior of a Class of Genetic Adaptive Systems, Ph.D. thesis, University of Michigan, Ann Arbor (1975)
Randy, L., Haupt, S.: Practical Genetic Algorithms. Wiley-IEEE Publication (2003)
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Ramezani, F., Lotfi, S. (2011). IAMGA: Intimate-Based Assortative Mating Genetic Algorithm. In: Panigrahi, B.K., Suganthan, P.N., Das, S., Satapathy, S.C. (eds) Swarm, Evolutionary, and Memetic Computing. SEMCCO 2011. Lecture Notes in Computer Science, vol 7076. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27172-4_30
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DOI: https://doi.org/10.1007/978-3-642-27172-4_30
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