D. Vra jitoru
[1] J.H. Holland, Adaptation in natural and artificial systems(Ann Arbor, USA: University of Michigan Press, 1975). [2] D.E. Goldberg, Genetic algorithms in search, optimization,and machine learning (Addison-Wesley, USA: Reading, 1989). [3] B.H. Sumida, A.I. Huston, J.M. McNamara, & W.D. Hamil-ton, Genetic algorithms and evolution, Journal of TheoreticalBiology, 147, 1990, 59–84. doi:10.1016/S0022-5193(05)80252-8 [4] G.F. Miller & P.M. Todd, The role of mate choice in bio-computation: Sexual selection as a process of search, opti-mization, and diversification, in W. Banzhaf & F.H. Eeckman,(Eds.), Evolution and biocomputation: Computational modelsof evolution (Berlin: Springer-Verlag, 1995), 169–204. [5] J. Ventrella, Sexual swimmers: Emergent morphology andlocomotion without a fitness function. in Proceedings of theFourth International Conference on Simulation of Adaptive Be-havior (Cambridge, USA: MIT Press/Bradford Books, 1996),484–496. [6] C.K. Hemelrijk, Effects of cohesiveness on intersexual domi-nance relationships and spatial structure among group-livingvirtual entities, Proc. Eur. Conf. on Artificial Life V, SpringerVerlag, 1999, 524–534. [7] D. Vrajitoru, Simulating gender separation with genetic algo-rithms. in Proceedings of the Genetic and Evolutionary Com-putation Conference (Morgan Kaufmann Publishers, 2002),634–641. [8] J. Noble, Sexual signalling in an artificial population: Whendoes the handicap principle work? in D. Floreano, F. Mondada,& J.-D. Nicoud, Proceeding of the European Conference onArtificial Life V, Springer Verlag, 1999, 644–653. [9] J. Rejeb & M. AbuElhaija, New gender genetic algorithmfor solving graph partitioning problems, 43rd IEEE MidwestSymposium on Circuits and Systems, Vol. 1, IEEE, 2000,444–446. [10] S. Naeem, L.J. Thompson, S.P. Lawlor, J.H. Lawton, & R.M.Woodfin, Declining biodiversity can alter the performance ofecosystems, Nature, 386, 1994, 734–737. doi:10.1038/368734a0 [11] N.D. Martinez, Defining and measuring functional aspects ofbiodiversity, Biodiversity: A biology of numbers and difference(Oxford, UK: Blackwell Scientific, 1996), 114–148. [12] J.D. Schaffer & L. Eshelman, On crossover as an evolutionaryviable startegy, in R. Belew & L. Booker, (Eds.), Proceedingsof the Fourth International Conference on Genetic Algorithms(San Mateo, USA: Morgan Kaufmann Publishers, 1991), 61–68. [13] W.D. Hamilton, R. Axelrod, & R. Tanese, Sexual reproductionas an adaptation to resist parasites, Proceedings of the NationalAcademy of Sciences, 87, May 1990, 3566–3573. doi:10.1073/pnas.87.9.3566 [14] G.M. Werner & P.M. Todd, Too many love songs: Sexualselection and the evolution of communication, in P. Husbands& I. Harvey, (Eds.), The Fourth European Conference onArtificial Life (Cambridge, USA: MIT Press/Bradford Books,1997). [15] R. Allenson, Genetic algorithms with gender for multi-functionoptimisation, Technical Report EPCC-SS92-01 (Edinburgh,Scotland: Edinburgh Parallel Computing Centre, 1992). [16] J. Sanchez-Velazco & J.A. Bullinaria, Sexual selection withcompetitive/co-operative operators for genetic algorithms,IASTED International Conference on Neural Networks andComputational Intelligence (NCI 2003) (Cancun, Mexico:ACTA Press, 2003). [17] D. Whitley, K. Mathias, S. Rana, & J. Dzubera, Evaluatingevolutionary algorithms, Artificial Intelligence, 85, 1996, 245–276. doi:10.1016/0004-3702(95)00124-7 [18] D. Vrajitoru, Simulating gender separation and mating con-straints for genetic algorithms, Technical Report TR-20050520-1 (South Bend, USA: Indiana University South Bend, 2005). [19] K. Deb & D.E. Goldberg, Sufficient conditions for arbitrarybinary functions. Annals of Mathematics and Artificial Intel-ligence, 10, 1994, 385–408. doi:10.1007/BF01531277 [20] C.K. Mohan, Selective crossover: Towards fitter offspring.Proc. Symp. Appl. Computing (SAC’98), Atlanta, USA 1998. [21] K. De Jong & M. Spears, Using genetic algorithms to solveNP-complete problems, Proc. Int. Conf. on Genetic Algorithms(Fairfax: George Mason University, 1989), 124–132. [22] D. Vrajitoru, Genetic programming operators applied to geneticalgorithms, in Proceedings of the Genetic and EvolutionaryComputation Conference (Orlando, USA: Morgan KaufmannPublishers, 1999), 686–693. [23] D. Vrajitoru, Intra and extra-generation schemes for combiningcrossover operators, in The Midwest Artificial Intelligence andCognitive Science Conference, Roosevelt University, IL, 2004,86–91. [24] K. De Jong, An analysis of the behaviour of a class of geneticadaptive systems, Ph.D. Thesis, University of Michigan, 1975. [25] G. Syswerda, Uniform crossover in genetic algorithms, in J.D.Schaffer, (Ed.), Proceedings of the International Conferenceon Genetic Algorithms (San Mateo, USA: Morgan KaufmannPublishers, 1989).
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