Abstract
Tournament selection performs tournaments by first sampling individuals uniformly at random from the population and then selecting the best of the sample for some genetic operation. This sampling process needs to be repeated many times when creating a new generation. However, even upon iteration, it may happen not to sample some of the individuals in the population. These individuals can therefore play no role in future generations. Under conditions of low selection pressure, the fraction of individuals not involved in any way in the selection process may be substantial. In this paper we investigate how we can model this process and we explore the possibility, methods and consequences of not generating and evaluating those individuals with the aim of increasing the efficiency of evolutionary algorithms based on tournament selection. In some conditions, considerable savings in terms of fitness evaluations are easily achievable, without altering in any way the expected behaviour of such algorithms.
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References
Bäck, T., Fogel, D.B., Michalewicz, T. (eds.): Evolutionary Computation 1: Basic Algorithms and Operators. Institute of Physics Publishing (2000)
Blickle, T., Thiele, L.: A mathematical analysis of tournament selection. In: Eshelman, L.J. (ed.) Proceedings of the Sixth International Conference on Genetic Algorithms (ICGA 1995), San Francisco, California, pp. 9–16. Morgan Kaufmann Publishers, San Francisco (1995)
Blickle, T., Thiele, L.: A comparison of selection schemes used in evolutionary algorithms. Evolutionary Computation 4(4), 361–394 (1997)
Davis, T.E., Principe, J.C.: A Markov chain framework for the simple genetic algorithm. Evolutionary Computation 1(3), 269–288 (1993)
De Jong, K.A., Spears, W.M., Gordon, D.F.: Using Markov chains to analyze GAFOs. In: Darrell Whitley, L., Vose, M.D. (eds.) Proceedings of the Third Workshop on Foundations of Genetic Algorithms, July 31–August 2, pp. 115–138. Morgan Kaufmann, San Francisco (1995)
Feller, W.: An Introduction to Probability Theory and Its Applications, vol. 2. John Wiley, Chichester (1971)
Mitchell, M.: An introduction to genetic algorithms. MIT Press, Cambridge (1996)
Motoki, T.: Calculating the expected loss of diversity of selection schemes. Evolutionary Computation 10(4), 397–422 (2002)
Nix, A.E., Vose, M.D.: Modeling genetic algorithms with Markov chains. Annals of Mathematics and Artificial Intelligence 5, 79–88 (1992)
Poli, R., Langdon, W.B.: Backward-chaining genetic programming (2005)
Poli, R., Rowe, J.E., McPhee, N.F.: Markov chain models for GP and variable-length GAs with homologous crossover. In: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2001), San Francisco, California, USA, July 7-11, Morgan Kaufmann, San Francisco (2001)
Rudolph, G.: Convergence analysis of canonical genetic algorithm. IEEE Transactions on Neural Networks 5(1), 96–101 (1994)
Russell, S.J., Norvig, P.: Artificial Intelligence: A Modern Approach, 2nd edn. Prentice Hall, Englewood Cliffs (2003)
Teller, A., Andre, D.: Automatically choosing the number of fitness cases: The rational allocation of trials. In: Koza, J.R., Deb, K., Dorigo, M., Fogel, D.B., Garzon, M., Iba, H., Riolo, R.L. (eds.) Genetic Programming 1997: Proceedings of the Second Annual Conference, Stanford University, CA, USA, July 13-16, pp. 321–328. Morgan Kaufmann, San Francisco (1997), http://www.cs.cmu.edu/afs/cs/usr/astro/public/papers/GR.ps
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Poli, R. (2005). Tournament Selection, Iterated Coupon-Collection Problem, and Backward-Chaining Evolutionary Algorithms. In: Wright, A.H., Vose, M.D., De Jong, K.A., Schmitt, L.M. (eds) Foundations of Genetic Algorithms. FOGA 2005. Lecture Notes in Computer Science, vol 3469. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11513575_8
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DOI: https://doi.org/10.1007/11513575_8
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