Assessment Methodologies for Multiobjective Evolutionary Algorithms

作者: Ruhul Sarker , Carlos A. Coello Coello

DOI: 10.1007/0-306-48041-7_7

关键词:

摘要: The Pareto-based evolutionary multiobjective algorithms have shown some success in solving optimization problems. However, it is difficult to judge the performance of because there no universally accepted definition optimum as single-objective As appeared literature, are several methods compare two or more algorithms. In this chapter, we discuss existing comparison with their strengths and weaknesses.

参考文章(33)
Simon Ronald, Robust Encodings in Genetic Algorithms Springer Berlin Heidelberg. pp. 29- 44 ,(1997) , 10.1007/978-3-662-03423-1_2
T. Brune, C.K. Kuek, M. Chowdhury, Y. Li, W. Feng, L. Chan, Benchmarks for testing evolutionary algorithms ,(1998)
Richard A. Caruana, J. David Schaffer, Representation and Hidden Bias: Gray vs. Binary Coding for Genetic Algorithms international conference on machine learning. pp. 153- 161 ,(1988) , 10.1016/B978-0-934613-64-4.50021-9
J. David Schaffer, Proceedings of the third international conference on Genetic algorithms international conference on genetic algorithms. ,(1989)
Terence C. Fogarty, Varying the Probability of Mutation in the Genetic Algorithm international conference on genetic algorithms. pp. 104- 109 ,(1989)
Andrzej Jaszkiewicz, Maciej Hapke, Paweł Kominek, None, Performance of Multiple Objective Evolutionary Algorithms on a Distribution System Design Problem - Computational Experiment international conference on evolutionary multi criterion optimization. pp. 241- 255 ,(2001) , 10.1007/3-540-44719-9_17
Richard A. Caruana, J. David Schaffer, Larry J. Eshelman, Biases in the crossover landscape international conference on genetic algorithms. pp. 10- 19 ,(1989)
Jason R. Schott, Fault Tolerant Design Using Single and Multicriteria Genetic Algorithm Optimization. Massachusetts Institute of Technology. ,(1995)
Viviane Grunert da Fonseca, Carlos M. Fonseca, Andreia O. Hall, Inferential Performance Assessment of Stochastic Optimisers and the Attainment Function international conference on evolutionary multi criterion optimization. pp. 213- 225 ,(2001) , 10.1007/3-540-44719-9_15