Abstract
The Efficient Global Optimization (EGO) is capable of using limited function evaluation budget to find the global optimum. However, EGO is by design not built for parallelization, which is an important technique to speed up the costly computer codes. Some approaches have been developed to fix this issue. e.g. Constant Liar Strategy. In this article we propose an alternative way to obtain multiple points in the Efficient Global Optimization cycle, where a niching evolution strategy is combined into the classic EGO framework. The new approach is discussed and compared to other methods which aim at the same goal. The proposed approach is also experimented on the selected test functions.
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References
Chevalier, C., Ginsbourger, D.: Fast Computation of the multi-points expected improvement with applications in batch selection, pp. 59–69. Springer, Heidelberg (2013)
De Jong, K.A.: An analysis of the behavior of a class of genetic adaptive systems. Ph.D. thesis, Ann Arbor, MI, USA, (1975). AAI7609381
Ginsbourger, D., Le Riche, R., Carraro, L.: Kriging is well-suited to parallelize optimization. In: Computational Intelligence in Expensive Optimization Problems, pp. 131–162. Springer (2010)
Goldberg, D.E., Richardson, J.: Genetic algorithms with sharing for multimodal function optimization. In: Genetic Algorithms and Their Applications: Proceedings of the Second International Conference on Genetic Algorithms, pp. 41–49. Lawrence Erlbaum, Hillsdale (1987)
Hansen, N., Ostermeier, A.: Completely derandomized self-adaptation in evolution strategies. Evol. Comput. 9(2), 159–195 (2001)
Horn, D., Wagner, T., Biermann, D., Weihs, C., Bischl, B.: Model-based multi-objective optimization: taxonomy, multi-point proposal, toolbox and benchmark. In: Evolutionary Multi-Criterion Optimization, pp. 64–78. Springer (2015)
Jones, D.R., Schonlau, M., Welch, W.J.: Efficient global optimization of expensive black-box functions. J. Glob. Optim. 13(4), 455–492 (1998)
Krige, D.G.: A statistical approach to some basic mine valuation problems on the witwatersrand. J. Chem. Metall. Min. Soc. S. Africa 52(6), 119–139 (1951)
Miller, B.L., Shaw, M.J.: Genetic algorithms with dynamic niche sharing for multimodal function optimization. In: 1996 Proceedings of IEEE International Conference on Evolutionary Computation, pp. 786–791. IEEE (1996)
Mockus, J.B., Mockus, L.J.: Bayesian approach to global optimization and application to multiobjective and constrained problems. J. Optim. Theor. Appl. 70(1), 157–172 (1991)
Ostermeier, A., Gawelczyk, A., Hansen, N.: Step-size adaption based on non-local use of selection information. In: Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: Parallel Problem Solving from Nature, PPSN III, pp. 189–198, Springer, London (1994)
Ponweiser, W., Wagner, T., Biermann, D., Vincze, M.: Multiobjective optimization on a limited budget of evaluations using model-assisted \(\cal{S}\)-metric selection. In: Parallel Problem Solving from Nature–PPSN X, pp. 784–794. Springer (2008)
Rasmussen, C.E., Williams, C.K.: Gaussian Processes for Machine Learning. Adaptive Computation and Machine Learning. MIT Press, Cambridge (2006)
Roustant, O., Ginsbourger, D., Deville, Y.: DiceKriging, DiceOptim: two R packages for the analysis of computer experiments by kriging-based metamodeling and optimization. J. Stat. Softw. 51(1), 1–55 (2012)
Sacks, J., Welch, W.J., Mitchell, T.J., Wynn, H.P.: Design and analysis of computer experiments. Stat. Sci. 4(4), 409–423 (1989)
Santner, T.J., Williams, B.J., Notz, W.: The Design and Analysis of Computer Experiments. Springer Series in Statistics. Springer (2003)
Schonlau, M.: Computer Experiments and Global Optimization. University of Waterloo (1998)
Shir, O.M., Bäck, T.: Dynamic niching in evolution strategies with covariance matrix adaptation. In: The 2005 IEEE Congress on Evolutionary Computation, 2005, vol. 3, pp. 2584–2591. IEEE (2005)
Shir, O.M., Bäck, T.: Niching in evolution strategies. In: Proceedings of the 7th Annual Conference on Genetic and Evolutionary Computation, pp. 915–916. ACM (2005)
Shir, O.M., Emmerich, M., Bäck, T.: Self-adaptive Niching CMA-ES with mahalanobis metric. In: IEEE Congress on Evolutionary Computation, 2007, CEC 2007, pp. 820–827. IEEE (2007)
Shir, O.M.: Niching in derandomized evolution strategies and its applications in quantum control. Natural Computing Group, LIACS, Faculty of Science, Leiden University (2008)
Zhigljavsky, A., Žilinskas, A.: Stochastic global optimization, vol. 9. Springer (2007)
Zaefferer, M., Bartz-Beielstein, T., Naujoks, B., Wagner, T., Emmerich, M.: A case study on multi-criteria optimization of an event detection software under limited budgets. In: Evolutionary Multi-Criterion Optimization, pp. 756–770. Springer (2013)
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The authors gratefully acknowledge financial support by the Netherlands Organisation for Scientific Research (NWO) within the project PROMIMOOC.
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Wang, H., Bäck, T., Emmerich, M.T.M. (2018). Multi-point Efficient Global Optimization Using Niching Evolution Strategy. In: Tantar, AA., Tantar, E., Emmerich, M., Legrand, P., Alboaie, L., Luchian, H. (eds) EVOLVE - A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation VI. Advances in Intelligent Systems and Computing, vol 674. Springer, Cham. https://doi.org/10.1007/978-3-319-69710-9_11
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DOI: https://doi.org/10.1007/978-3-319-69710-9_11
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