Robustness in multi-objective optimization using evolutionary algorithms

Author:

Gaspar-Cunha A.,Covas J. A.

Publisher

Springer Science and Business Media LLC

Subject

Applied Mathematics,Computational Mathematics,Control and Optimization

Reference23 articles.

1. Schaffer, J.D.: Some experiments in machine learning using vector evaluated genetic algorithms. Ph.D. thesis, Vanderbilt University, Nashville, TN (1984)

2. Fonseca, C.M., Fleming, P.J.: Genetic algorithms for multiobjective optimization: formulation, discussion and generalization. In: Proceedings of the Fifth International Conference on Genetic Algorithms, pp. 416–423. Morgan Kauffman, Los Altos (1993)

3. Srinivas, N., Deb, K.: Multiobjective optimization using nondominated sorting in genetic algorithms. Evol. Comput. 2, 221–248 (1995)

4. Horn, J., Nafpliotis, N., Goldberg, D.E.: A niched Pareto genetic algorithm for multiobjective optimization, In: Proceedings of the First IEEE Conference on Evolutionary Computation, pp. 82–87 (1994)

5. Deb, K., Pratap, A., Agrawal, S., Meyarivan, T.: A fast and elitist multi-objective genetic algorithm: NSGAII. IEEE Trans. Evol. Comput. 6, 182–197 (2002)

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