International Journal of Intelligent Systems and Applications (IJISA)
IJISA Vol. 8, No. 9, 8 Sep. 2016
Cover page and Table of Contents: PDF (size: 474KB)
The Effects of “Preferentialism” on a Genetic Algorithm Population over Elitism and Regular Development in a Binary F6 Fitness Function
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Author(s)
Index Terms
Genetic Algorithms, Preferentialism, Elitism, BinaryF6, Natural Elitism
Abstract
Mating preferentialism among animals is the natural form of elitism that has a higher genetic variance and a shorter number of interactions. This concept refers to fact that most animals cannot breed indefinitely – this is the case of elitism - and suffer DNA degradation. In this paper, two types of preferentialism were analyzed (mutation and second best); in both cases we found evidence of improvements over no-preferentialism or elitism. The best number of generations for preferentialism was determined to be 5, from a group of 3 to 20, with the smallest average of iterations and the most consistent average fitness. A sequencing of 0 to 7 was selected and used in association with mutation preferentialism in order to determine the best number of generations. In the case of BinaryF6, mutation preferentialism has a higher average best fitness (ABF) (0.9986) and a lower number of interactions (2259). Second best preferentialism has a better average last fitness (ALF) (0.6070) and a little higher number of interactions (3956). These results reveal that the two suggested form of preferentialism exhibit significant improvements in terms of time and result quality when they are compared with elitism (ABF of 0.9981, ALF of 0.6005 and an average number of interactions of 18197) or with no-preferentialism (ABF of 0.9982, ALF of 0.5177 and average number of interactions of 181088.
Cite This Paper
Julia Naomi Rosenfield Boeira, "The Effects of "Preferentialism" on a Genetic Algorithm Population over Elitism and Regular Development in a Binary F6 Fitness Function", International Journal of Intelligent Systems and Applications (IJISA), Vol.8, No.9, pp.38-46, 2016. DOI:10.5815/ijisa.2016.09.05
Reference
[1]S. Alberts, H. Watts and J. Altmann, "Queuing and queue-jumping: long-term patterns of reproductive skew in male savannah baboons, Papio cynocephalus," Animal Behavior, vol. 65, no. 4, 2003.
[2]C. Parker, "Male dominance and reproductive activity in Papio anubis," Animal Behavior, vol. 27, 1979.
[3]S. Hrdy, The Langurs of Abu: Female and Male Strategies of Reproduction, Boston: Harvard University Press, 1980.
[4]M. Ramenofsky, R. Hegner, A. Dufty and J. Wingfield, "Testosterone and Agression in Birds," American Scientist, vol. 75, no. 6, 1987.
[5]J. Pemberton, T. Clutton-Brock, J. Smith and D. Coltmann, "Male reproductive success in a promiscuous mammal: behavioural estimates compared with genetic paternity," Molecular Ecology, vol. 8, no. 7, 1999.
[6]M. Muller, M. Thompson and R. Wrangham, "Male Chimpanzees Prefer Mating with Old Females," Current Biology, vol. 16, no. 22, 2006.
[7]B. Le Bouef, "Male-male Competition and Reproductive Success in Elephant Seals," Amer. Zoo., vol. 14, no. 1, 1974.
[8]S. Creel and K. Rabenold, "Inclusive fitness and reproductive strategies in dwarf mongooses," Behavioral Ecology, vol. 5, no. 3, 1994.
[9]A. De Loof, "Longevity and aging in insects: Is reproduction costly; cheap; beneficial or irrelevant? A critical evaluation of the “trade-off” concept," Journal of Insect Physiology, vol. 59, no. 1, 2011.
[10]L. Amaral and E. Hruschka Jr, "Transgenic: An evolutionary algorithm operator," Neurocomputing, vol. 127, no. 15, 2014.
[11]L. Barreiro and J. Brinkworth, "The contribution of natural selection to present-day susceptibility to chronic inflammatory and autoimmune disease," Current Opinion in Immunology, vol. 31, 2014.
[12]C. Sapienza, E. de la Casa-Esperon and F. de Villena, "Natural selection and the function of genome imprinting: beyond the silenced minority," Trends in Genetics, vol. 16, no. 12, 2000.
[13]A. Biswas, S. Das, A. Abraham and S. Dasgupta, "Stability analysis of the reproduction operator in bacterial foraging optimization," Theoretical Computer Science, vol. 411, no. 21, 2010.
[14]B. Niu, J. Wang and H. Wang, "Bacterial-inspired algorithms for solving constrained optimization problems," Neurocomputing, vol. 148, no. 19, 2015.
[15]C. Ozturk, E. Hancer and D. Karaboga, "A novel binary artificial bee colony algorithm based on genetic operators," Information Sciences, vol. 297, no. 10, 2015.
[16]M. Mitchell, "L.D. Davis, Handbook of Genetic Algorithms*," Artificial Intelligence , vol. 100, 1998.
[17]M. Gibbs, H. Maier and G. Dandy, "Using characteristics of the optimisation problem to determine the Genetic Algorithm population size when the number of evaluations is limited," Environmental Modelling & Software, vol. 69, 2015.
[18]S. Russel and P. Norvig, Artificial Inteligance, USA: Prentice Hall, 2010.
[19]M. Nayeem, M. Rahman and M. Rahman, "Transit network design by genetic algorithm with elitism," Transportation Research, vol. 46, 2014.
[20]M. Wibig, "Dynamic Programming and Genetic Algorithm for Business Processes Optimisation," I.J. Intelligent Systems and Applications, vol. 1, pp. 44-51, 2013.
[21]H. Mühlenbein, "Parallel genetic algorithms, population genetics and combinatorial optimization," Parallelism, Learning, Evolution, vol. 565, 2005.
[22]J. Wang, O. Ersoy, M. He and F. Wang, "Multi-offspring genetic algorithm and its application to the traveling salesman problem," Applied soft Computing, vol. 43, 2016.
[23]M. Kuhn, T. Severin and H. Salzwedel, "Variable Mutation Rate at Genetic Algorithms: Introduction of Chromosome Fitness in Connection with Multi-Chromosome Representation," International Journal of Computer Applications, vol. 752, no. 17, 13.
[24]Y. Yun, "Hybrid genetic algorithm with adaptive local search scheme," Computers & Industrial Engineering, vol. 51, no. 1, 2006.
[25]L. Davis, Handbook of genetic algorithms, New York: Van Nostrand Reinhold, 1991.
[26]L. Zang and T. Wong, "An object-coding genetic algorithm for integrated process planning and scheduling," European Journal of Operational Research, vol. 244, no. 2, 2015.
[27]C. C. Reeves, "A Genetic Algorithm for Flowshop Sequencing," Computers Ops Res., vol. 22, no. Pergamon, 1995.
[28]S. Austad, "Menopause: An evolutionary perspective," Experimental Gerontology, vol. 29, no. 3-4, 1994.
[29]A. Lipowski and D. Lipowski, "Roulette-wheel selection via stochastic acceptance," Physica A: Statistical Mechanics and its Applications, vol. 391, no. 6, 2012.
[30]R. Zbieć-Piekarska and W. Branicki, "Examination of DNA methylation status of the ELOVL2 marker may be useful for human age prediction in forensic science," Forensic Science International: Genetics, vol. 14, 2015.
[31]P. Victer Paul, N. Moganarangan, S. Kumar, R. Raju and P. Dhavachelvand, "Performance analyses over population seeding techniques of the permutation-coded genetic algorithm: An empirical study based on traveling salesman problems," Applied Soft Computing, vol. 32, 2015.
[32]Z. Ma, "Chaotic populations in genetic algorithms," Applied Soft Computing, vol. 12, no. 8, 2012.
[33]A. Shakarneh, "A Review of Genetic Algorithm Optimization: Operations and Applications to Water Pipeline Systems," International Journal of Mathematical, Computational, Physical and Quantum Engineering, vol. 7, no. 12, 2013.
[34]C. Chitra and P. Subbaraj, "A nondominated sorting genetic algorithm solution for shortest path routing problem in computer networks," Expert Systems with Applications, vol. 39, no. 1, p. 1518–1525, 2012.
[35]D. Goldberg and B. Miller, "Genetic Algorithms, Tournament Selection, and the Effects of Noise," Complex System, 1995.
[36]A. Sinclair and P. Arcese, Serengeti II - Dynamics, Management and Conservation of an Ecosystem., Chicago and London: The Chicago University Press, 1995.
[37]L. Gesquiere, N. Learn and J. Altmann, "Life at the Top: Rank and Stress in Wild Male Baboons," Science, vol. 333, 2011.
[38]A. Bradley, I. McDonald and A. Lee, "Stress and mortality in a small marsupial (Antechinus stuartii, Macleay)," General and Comparative Endocrinology, vol. 40, 1980.
[39]O. Dellagostin, R. Bastos and J. Deschamps, "Efeito do Gene do Estresse Suíno sobre Características de Quantidade e Qualidade de Carcaça," Rv. Bras. Zootec., vol. 30, 2001.
[40]D. S. Weile and E. Michielssen, "Genetic Algorithm Optimization Applied to Electromagnetics: A Review," TRANSACTIONS ON ANTENNAS AND PROPAGATION, vol. 45, no. 3, 1997.
[41]T.-W. Lam, K. Sadakane and W.-K. Sung, "A Space and Time Efficient Algorithm for Constructing Compressed Suffix Arrays," Computing and Combinatorics, vol. 2387, 2002.
[42]X. Huang and W. Miller, "A time-efficient, linear-space local similarity algorithm," Avances in Applied Mathmatics, vol. 12, no. 3, 1991.
[43]T. Meyarivan, S. Agarwal, A. Pratap and K. Deb, "A Fast and Elitist Multiobjective Genetic Algorithm: NSGA-II," IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION,, vol. 6, 2002.