Abstract
We present an empirical study on the impact of different design choices on the performance of an evolutionary algorithm (EA). Four EA components are considered—parent selection, survivor selection, recombination and mutation—and for each component we study the impact of choosing the right operator, and of tuning its free parameter(s). We tune 120 different combinations of EA operators to 4 different classes of fitness landscapes, and measure the cost of tuning. We find that components differ greatly in importance. Typically the choice of operator for parent selection has the greatest impact, and mutation needs the most tuning. Regarding individual EAs however, the impact of design choices for one component depends on the choices for other components, as well as on the available amount of resources for tuning.
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References
Birattari, M.: The Problem of Tuning Metaheuristics as Seen from a Machine Learning Perspective. PhD thesis, Université Libre de Bruxelles (2004)
Czarn, A., MacNish, C., Vijayan, K., Turlach, B.A., Gupta, R.: Statistical Exploratory Analysis of Genetic Algorithms. IEEE Trans. Evol. Comp. 8(4), 405–421 (2004)
Eiben, A.E., Hinterding, R., Michalewicz, Z.: Parameter Control in Evolutionary Algorithms. IEEE Trans. Evol. Comput. 3(2), 124–141 (1999)
Eiben, A.E., Michalewicz, Z., Schoenauer, M., Smith, J.E.: Parameter Control in Evolutionary Algorithms. In: Lobo, et al. (eds.) [12], pp. 19–46
Eiben, A.E., Smith, J.E.: Introduction to Evolutionary Computing. Springer, Heidelberg (2003)
François, O., Lavergne, C.: Design of Evolutionary Algorithms—A Statistical Perspective. IEEE Trans. Evol. Comput. 5(2), 129–148 (2001)
Friesleben, B., Hartfelder, M.: Optimization of Genetic Algorithms by Genetic Algorithms. In: Albrecht, R.F., Reeves, C.R., Steele, N.C. (eds.) Artificial Neural Networks and Genetic Algorithms, pp. 392–399. Springer, Heidelberg (1993)
Gallagher, M., Yuan, B.: A General-Purpose Tunable Landscape Editor. IEEE Trans. Evol. Comput. 10(5), 590–603 (2006)
Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley Longman Publishing Co., Boston (1989)
Grefenstette, J.J.: Optimization of Control Parameters for Genetic Algorithms. IEEE Trans. Syst. Man Cybernet. 16(1), 122–128 (1986)
De Jong, K.A.: An Analysis of the Behaviour of a Class of Genetic Adaptive Systems. PhD thesis, University of Michigan (1975)
Lobo, F.G., Lima, C.F., Michalewicz, Z. (eds.): Parameter Setting in Evolutionary Algorithms. Studies in Computational Intelligence. Springer, Heidelberg (2007)
Luke, S., et al.: A Java-based Evolutionary Computation Research System, http://www.cs.gmu.edu/~eclab/projects/ecj/
Mühlenbein, H., Höns, R.: The Estimation of Distributions and the Minimum Relative Entropy Principle. Evolutionary Computation 13(1), 1–27 (2005)
Nannen, V., Eiben, A.E.: Efficient Relevance Estimation and Value Calibration of Evolutionary Algorithm Parameters. In: IEEE Congress on Evolutionary Computation (CEC), Piscataway, NJ, USA. IEEE Press, Los Alamitos (2007)
Nannen, V., Eiben, A.E.: Relevance Estimation and Value Calibration of Evolutionary Algorithm Parameters. In: Veloso, M.M., et al. (eds.) Proc. of the 20th Int. Joint Conf. on Artif. Intell., IJCAI 2007, pp. 1034–1039. AAAI Press, Menlo Park (2007)
Oliver, I.M., Smith, D.J., Holland, J.R.C.: A Study of Permutation Crossover Operators on the Traveling Salesman Problem. In: Grefenstette, J.J. (ed.) Proc. of the 2nd Int. Conf. on Genetic Algorithms on Genetic algorithms and their application, pp. 224–230. L. E. Associates (1987)
Preuss, M., Bartz-Beielstein, T.: Sequential Parameter Optimization Applied to Self-adaptation for Binary-coded Evolutionary Algorithms. In: [12], pp. 91–119
Rudolph, G.: On Correlated Mutations in Evolution Strategies. In: Männer, R., Manderick, B. (eds.) Proc. of the 2nd Conf. on Parallel Problem Solving from Nature, pp. 107–116. Springer, Heidelberg (1992)
Samples, M.E., Byom, M.J., Daida, J.M.: Parameter Sweeps for Exploring Parameter Spaces of Genetic and Evolutionary Algorithms. In: [12], pp. 161–184
Schaffer, J.D., Caruana, R.A., Eshelman, L.J., Das, R.: A Study of Control Parameters Affecting Online Performance of Genetic Algorithms for Function Optimization. In: Schaffer, J.D. (ed.) Proc. of the 3rd Int. Conf. on Genetic Algorithms, pp. 51–60. Morgan Kaufmann, San Francisco (1989)
Taguchi, G., Wu, Y.: Introduction to Off-Line Quality Control. Central Japan Quality Control Association, Nagoya, Japan (1980)
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Nannen, V., Smit, S.K., Eiben, A.E. (2008). Costs and Benefits of Tuning Parameters of Evolutionary Algorithms. In: Rudolph, G., Jansen, T., Beume, N., Lucas, S., Poloni, C. (eds) Parallel Problem Solving from Nature – PPSN X. PPSN 2008. Lecture Notes in Computer Science, vol 5199. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87700-4_53
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DOI: https://doi.org/10.1007/978-3-540-87700-4_53
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