Abstract
Many practical optimization problems are subject to uncertain fitness evaluations. One way to reduce the noise is to average over multiple samples of the fitness function in order to evaluate a single individual. This paper proposes a general way to integrate statistical ranking and selection procedures into evolutionary algorithms. The proposed procedure focuses sampling on those individuals that are crucial for the evolutionary algorithm, and distributes samples in a way that efficiently reduces uncertainty. The goal is to drastically reduce the number of evaluations required for a proper operation of the evolutionary algorithm in noisy environments.
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References
Arnold, D.V., Beyer, H.-G.: A comparison of evolution strategies with other direct search methods in the presence of noise. Computational Optimization and Applications 24, 135–159 (2003)
Bartz-Beielstein, T., Blum, D., Branke, J.: Particle swarm optimization and sequential sampling in noisy environments. In: Metaheuristics International Conference (2005)
Beyer, H.-G.: Toward a theory of evolution strategies: Some asymptotical results from the (1 +, λ)-theory. Evolutionary Computation 1(2), 165–188 (1993)
Boesel, J.: Search and Selection for Large-Scale Stochastic Optimization. PhD thesis, Northwestern University, Evanston, Illinois, USA (1999)
Boesel, J., Nelson, B.L., Kim, S.H.: Usint ranking and selection to ”clean up” after simulation optimization. Operations Research 51, 814–825 (2003)
Branke, J.: Evolutionary Optimization in Dynamic Environments. Kluwer, Dordrecht (2001)
Branke, J., Chick, S., Schmidt, C.: New developments in ranking and selection: An empirical comparison of the three main approaches. In: Kuhl, N.E., Steiger, M.N., Armstrong, F.B., Joines, J.A. (eds.) Winter Simulation Conference, pp. 708–717. IEEE, Los Alamitos (2005)
Branke, J., Schmidt, C.: Selection in the presence of noise. In: Cantú-Paz, E., Foster, J.A., Deb, K., Davis, L., Roy, R., O’Reilly, U.-M., Beyer, H.-G., Kendall, G., Wilson, S.W., Harman, M., Wegener, J., Dasgupta, D., Potter, M.A., Schultz, A., Dowsland, K.A., Jonoska, N., Miller, J., Standish, R.K. (eds.) GECCO 2003. LNCS, vol. 2723, pp. 766–777. Springer, Heidelberg (2003)
Branke, J., Schmidt, C.: Sequential sampling in noisy environments. In: Yao, X., Burke, E.K., Lozano, J.A., Smith, J., Merelo-Guervós, J.J., Bullinaria, J.A., Rowe, J.E., Tiňo, P., Kabán, A., Schwefel, H.-P. (eds.) PPSN 2004. LNCS, vol. 3242, pp. 202–211. Springer, Heidelberg (2004)
Buchholz, P., Thümmler, A.: Enhancing evolutionary algorithms with statistical sselection procedures for simulation optimization (2005)
Cantu-Paz, E.: Adaptive sampling for noisy problems. In: Deb, K., et al. (eds.) GECCO 2004. LNCS, vol. 3102, pp. 947–958. Springer, Heidelberg (2004)
Chen, C.-H.: A lower bound for the correct subset-selection probability and its application to discrete event simulations. IEEE Transactions on Automatic Control 41(8), 1227–1231 (1996)
Di Pietro, A., While, L., Barone, L.: Applying evolutionary algorithms to problems with noisy, time-consuming fitness functions. In: Congress on Evolutionary Computation, pp. 1254–1261. IEEE, Los Alamitos (2004)
Hammel, U., Bäck, T.: Evolution strategies on noisy functions, how to improveconvergence properties. In: Davidor, Y., Männer, R., Schwefel, H.-P. (eds.) PPSN 1994. LNCS, vol. 866. Springer, Heidelberg (1994)
Hedlund, H.E., Mollaghasemi, M.: A genetic algorithm and an indifference-zone ranking and selection framework for simulation optimization. In: Winter Simulation Conference, pp. 417–421. IEEE, Los Alamitos (2001)
Jin, Y., Branke, J.: Evolutionary optimization in uncertain environments – asurvey. IEEE Transactions on Evolutionary Computation 9(3), 303–317 (2005)
Stagge, P.: Averaging efficiently in the presence of noise. In: Eiben, A.E., Bäck, T., Schoenauer, M., Schwefel, H.-P. (eds.) PPSN 1998. LNCS, vol. 1498, pp. 188–197. Springer, Heidelberg (1998)
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Schmidt, C., Branke, J., Chick, S.E. (2006). Integrating Techniques from Statistical Ranking into Evolutionary Algorithms. In: Rothlauf, F., et al. Applications of Evolutionary Computing. EvoWorkshops 2006. Lecture Notes in Computer Science, vol 3907. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11732242_73
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DOI: https://doi.org/10.1007/11732242_73
Publisher Name: Springer, Berlin, Heidelberg
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