Global multiobjective optimization via estimation of distribution algorithm with biased initialization and crossover
Zhou A, Zhang Q, Jin Y, Sendhoff B, Tsang E, Lipson H (2007)
In: Proceedings of the 9th annual conference on Genetic and evolutionary computation. New York, NY, USA: ACM: 617-623.
Konferenzbeitrag
| Veröffentlicht | Englisch
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Autor*in
Zhou, Aimin;
Zhang, Qingfu;
Jin, YaochuUniBi 
;
Sendhoff, Bernhard;
Tsang, Edward;
Lipson, Hod
;
Sendhoff, Bernhard;
Tsang, Edward;
Lipson, HodAbstract / Bemerkung
Multiobjective optimization problems with many local Pareto fronts is a big challenge to evolutionary algorithms. In this paper, two operators, biased initialization and biased crossover, are proposed to improve the global search ability of RM-MEDA, a recently proposed multiobjective estimation of distribution algorithm. Biased initialization inserts several globally Pareto optimal solutions into the initial population; biased crossover combines the location information of some best solutions found so far and globally statistical information extracted from current population. Experiments have been conducted to study the effects of these two operators.
Erscheinungsjahr
2007
Titel des Konferenzbandes
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Seite(n)
617-623
Konferenz
GECCO07: Genetic and Evolutionary Computation Conference
Konferenzort
London England
Konferenzdatum
2007-07-07 – 2007-07-11
ISBN
9781595936974
Page URI
https://pub.uni-bielefeld.de/record/2978643
Zitieren
Zhou A, Zhang Q, Jin Y, Sendhoff B, Tsang E, Lipson H. Global multiobjective optimization via estimation of distribution algorithm with biased initialization and crossover. In: Proceedings of the 9th annual conference on Genetic and evolutionary computation. New York, NY, USA: ACM; 2007: 617-623.
Zhou, A., Zhang, Q., Jin, Y., Sendhoff, B., Tsang, E., & Lipson, H. (2007). Global multiobjective optimization via estimation of distribution algorithm with biased initialization and crossover. Proceedings of the 9th annual conference on Genetic and evolutionary computation, 617-623. New York, NY, USA: ACM. https://doi.org/10.1145/1276958.1277082
Zhou, Aimin, Zhang, Qingfu, Jin, Yaochu, Sendhoff, Bernhard, Tsang, Edward, and Lipson, Hod. 2007. “Global multiobjective optimization via estimation of distribution algorithm with biased initialization and crossover”. In Proceedings of the 9th annual conference on Genetic and evolutionary computation, 617-623. New York, NY, USA: ACM.
Zhou, A., Zhang, Q., Jin, Y., Sendhoff, B., Tsang, E., and Lipson, H. (2007). “Global multiobjective optimization via estimation of distribution algorithm with biased initialization and crossover” in Proceedings of the 9th annual conference on Genetic and evolutionary computation (New York, NY, USA: ACM), 617-623.
Zhou, A., et al., 2007. Global multiobjective optimization via estimation of distribution algorithm with biased initialization and crossover. In Proceedings of the 9th annual conference on Genetic and evolutionary computation. New York, NY, USA: ACM, pp. 617-623.
A. Zhou, et al., “Global multiobjective optimization via estimation of distribution algorithm with biased initialization and crossover”, Proceedings of the 9th annual conference on Genetic and evolutionary computation, New York, NY, USA: ACM, 2007, pp.617-623.
Zhou, A., Zhang, Q., Jin, Y., Sendhoff, B., Tsang, E., Lipson, H.: Global multiobjective optimization via estimation of distribution algorithm with biased initialization and crossover. Proceedings of the 9th annual conference on Genetic and evolutionary computation. p. 617-623. ACM, New York, NY, USA (2007).
Zhou, Aimin, Zhang, Qingfu, Jin, Yaochu, Sendhoff, Bernhard, Tsang, Edward, and Lipson, Hod. “Global multiobjective optimization via estimation of distribution algorithm with biased initialization and crossover”. Proceedings of the 9th annual conference on Genetic and evolutionary computation. New York, NY, USA: ACM, 2007. 617-623.
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