Using multi-objective evolutionary algorithms for single-objective constrained and unconstrained optimization-Reference-Cited by-同舟云学术

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Using multi-objective evolutionary algorithms for single-objective constrained and unconstrained optimization

Published:2015-09-22 Issue:1 Volume:240 Page:217-250
ISSN:0254-5330
Container-title:Annals of Operations Research
language:en
Short-container-title:Ann Oper Res

Author:

Segura Carlos,Coello Carlos A. Coello,Miranda Gara,León Coromoto

Funder

CONCYTEG

CONACyT

Publisher

Springer Science and Business Media LLC

Subject

Management Science and Operations Research,General Decision Sciences

Link

http://link.springer.com/content/pdf/10.1007/s10479-015-2017-z.pdf

Reference188 articles.

1. Abbass, H. A., & Deb, K. (2003). Searching under multi-evolutionary pressures. In Proceedings of the fourth conference on evolutionary multi-criterion optimization (pp. 391–404). Springer, Berlin.

2. Alba, E. (2005). Parallel metaheuristics: A new class of algorithms. New York: Wiley-Interscience.

3. Angantyr, A., Andersson, J., & Aidanpaa, J. O. (2003). Constrained Optimization based on a multiobjective evolutionary algorithm. In 2003 IEEE congress on evolutionary computation, Canberra, Australia, IEEE Service Center, Piscataway, New Jersey, CEC’03 (Vol. 3, pp. 1560–1567).

4. Back, T., Fogel, D. B., Michalewicz, Z. (Eds.) (1997). Handbook of evolutionary computation. IOP Publishing Ltd., Bristol, UK

5. Bandaru, S., & Deb, K. (2013). A parameterless-niching-assisted bi-objective approach to multimodal optimization. In 2013 IEEE Congress on evolutionary computation (CEC) (pp. 95–102).

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