Acceleration of Experiment-Based Evolutionary Multi-Objective Optimization Using Fitness Estimation
Author:
Affiliation:
1. Yamaha Motor Co., Ltd.
2. Kyoto University
Publisher
Institute of Electrical Engineers of Japan (IEE Japan)
Subject
Electrical and Electronic Engineering
Link
http://www.jstage.jst.go.jp/article/ieejeiss/128/3/128_3_388/_pdf
Reference26 articles.
1. (2) J. Branke and C. Schmidt: “Faster Convergence by means of fitness Estimation”, Soft Computing, Vol. 9-1, pp. 13-20 (2005)
2. (3) J. Branke, C. Schmidt, and H. Schmeck: “Efficient Fitness Estimation in Noisy Environments”, Proc. of GECCO 2001, pp. 243-250 (2001)
3. (4) K. Deb: Multi-Objective Optimization Using Evolutionary Algorithms, John Wiley & Sons (2001)
4. (5) K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan: “A Fast and Elitist Multiobjective Genetic Algorithm: NSGA-II”, IEEE Trans. on Evol. Comput., Vol. 6-2, pp. 182-197 (2002)
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2. Worst Case Prediction-based Differential Evolution For Multi-Noisy-Hard-objective Optimization Problems;IEEJ Transactions on Electronics, Information and Systems;2016
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