Introduction
In nature, evolution is mostly determined by natural selection of different individuals competing for resources in the environment. Those individuals that are better are more likely to survive and propagate their genetic material. The encoding for genetic information (genome) is done in a way that admits asexual reproduction, which results in offspring that are genetically identical to the parent. Sexual reproduction allows some exchange and re-ordering of chromosomes, producing offspring that contain a combination of information from each parent. This is the recombination operation, which is often referred to as crossover because of the way strands of chromosomes cross over during the exchange. The diversity in the population is achieved by mutation operation.
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References
Bäck, T., Fogel, D., Michalewicz, Z.: Handbook of evolutionary computation. IOP Publishing and Oxford University Press, New York (1997)
Eiben, A.E., Smith, J.E.: Introduction to Evolutionary Computing. Springer, Heidelberg (2003)
Eiben, A.E., Aarts, E.H.L., van Hee, K.M.: Global convergence of genetic algorithms: a markov chain analysis. In: Schwefel, H.-P., Männer, R. (eds.) PPSN 1990. LNCS, vol. 496, pp. 4–12. Springer, Heidelberg (1991)
Bäck, T.: Evolutionary algorithms in theory and practice. Oxford University Press, New York (1996)
Fogel, D.B.: Evolutionary Computation. IEEE Press, Los Alamitos (1995)
Bäck, T.: Generalized convergence models for tournament and (μ, λ) selection. In: Proceedings of the 6th International Conference on Genetic Algorithms, pp. 2–8 (1995)
Blickle, T., Thiele, L.: A comparison of selection schemes used in genetic algorithms. Evolutionary Computation 4(4), 361–394 (1996)
Goldberg, D.E., Deb, K.: A comparative analysis of selection schemes used in genetic algorithms. Foundations of Genetic Algorithms 1, 69–93 (1991)
Holland, J.H.: Adaptation in natural and artificial systems. MIT Press, Cambridge (1992)
Goldberg, D.E.: Generic algorithms in search, optimization and machine learning. Addison-Wesley, Reading (1989)
Baker, J.E.: Reducing Bias and Inefficiency in the Selection Algorithm. In: Proceedings of the Second International Conference on Genetic Algorithms and their Application, pp. 14–21. Lawrence Erlbaum Associates, Hillsdale (1987)
Evolutionary algorithms tutorial, http://www.geatbx.com/
Bäck, T., Hoffmeister, F.: Extended Selection Mechanisms in Genetic Algorithms. In: Bäck, T., Hoffmeister, F. (eds.) Proceedings of the Fourth International Conference on Genetic Algorithms, San Mateo, California, USA, pp. 92–99 (1991)
Whitley, D.: The GENITOR Algorithm and Selection Pressure: Why Rank-Based Allocation of Reproductive Trials is Best. In: Proceedings of the Third International Conference on Genetic Algorithms, San Mateo, California, USA, pp. 116–121 (1989)
Pohlheim, H.: Ein genetischer Algorithmus mit Mehrfachpopulationen zur Numerischen Optimierung. at-Automatisierungstechnik 3, 127–135 (1995)
Syswerda, G.: Uniform crossover in genetic algorithms. In: Proceedings of the Third International Conference on Genetic Algorithms, San Mateo, California, USA, pp. 2–9 (1989)
Spears, W.M., De Jong, K.A.: On the Virtues of Parameterised Uniform Crossover. In: Proceedings of the Fourth International Conference on Genetic Algorithms, San Mateo, California, USA, pp. 230–236 (1991)
Michalewicz, Z.: Genetic Algorithms + Data Structures = Evolution Programs. Springer, Berlin (1996)
Goldberg, D.E., Lingle, R.: Alleles, loci and the traveling salesman problem. In: Proceedings of the First International Conference on Genetic Algorithms and Their Applications, pp. 154–159. Lawrence Erlbaum, Hillsdale (1985)
Whitley, D.: Permutations, In Evolutionary Computation 1: Basic Algorithms and Operators. In: Bäck, T., Fogel, D.B. (eds.), pp. 274–284. Institute of Physics Publishing, Bristol (2000)
Davis, L. (ed.): Handbook of Genetic Algorithms. Van Nostrand Reinhold, New York (1991)
Olivier, L.M., Smith, D.J., Holland, J.: A study of permutation crossover operators on the traveling salesman problem. In: Proceedings of the Second International Conference on Genetic Algorithms and Their Applications, pp. 224–230. Lawrence Erlbaum, Hillsdale (1987)
Mühlenbein, H., Schlierkamp-Voosen, D.: Predictive Models for the Breeder Genetic Algorithm: I. Continuous Parameter Optimization. Evolutionary Computation 1(1), 25–49 (1993)
Yao, X., Liu, Y.: Fast evolutionary programming, In. In: Proceedings of the Fifth Annual Conference on Evolutionary Programming, pp. 451–460. The MIT Press, Cambridge (1996)
Mühlenbein, H., Pass, G.: From recombination of genes to the estimation of distributions I. Binary parameters. In: Proceedings of the 4th Conference on Parallel Problems Solving from Nature, pp. 188–197 (1996)
Rechenberg, I.: Evolutionsstrategie: Optimierung technischer Systeme nach Prinzipien der biologischen Evolution. Fromman-Holzboog, Stuttgart (1973)
Schwefel, H.P.: Numerische Optimierung von Computermodellen mittels der Evolutionsstrategie. Birkhaeuser, Basel (1977)
Fogel, L.J., Owens, A.J., Walsh, M.J.: Artificial intelligence through simulated evolution. Wiley, Chichester (1966)
Koza, J.R.: Genetic Programming: On the Programming of Computers by Means o f Natural Selection. MIT Press, Cambridge (1992)
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Grosan, C., Abraham, A. (2011). Evolutionary Algorithms. In: Intelligent Systems. Intelligent Systems Reference Library, vol 17. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21004-4_14
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