An hybrid neural/genetic approach to continuous multi-objective optimization problems

Mario Costa; Edmondo Minisci; Eros Pasero

doi:10.1007/978-3-540-45216-4_6

An hybrid neural/genetic approach to continuous multi-objective optimization problems

Mario Costa^*, Edmondo Minisci, Eros Pasero

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution book

5 Citations (Scopus)

Abstract

Evolutionary algorithms perform optimization using the information derived from a population of sample solution points. Recent developments in this field regard optimization as the evolutionary process of an explicit, probabilistic model of the search space. The algorithms derived on the basis of this new philosophy maintain every feature of the classic evolutionary algorithms, but arc able to overcome some drawbacks. In this paper an evolutionary multi-objective optimization tool based on an estimation of distribution algorithm is proposed. It uses the ranking method of non-dominated sorting genetic algorithm-II and the Parzen estimator to approximate the probability density of solutions lying on the Pareto front. The proposed algorithm has been applied to different types of test case problems and results show good performance of the overall optimization procedure in terms of the number of function evaluations.

Original language	English
Title of host publication	Neural Nets
Subtitle of host publication	14th Italian Workshop on Neural Nets, WIRN VIETRI 2003, Vietri sul Mare, Italy, June 4-7, 2003. Revised Papers
Editors	Bruno Apolloni, Maria Marinaro, Roberto Tagliaferri
Place of Publication	Berlin
Publisher	Springer
Pages	61-69
Number of pages	9
ISBN (Print)	9783540202271, 9783540452164
DOIs	https://doi.org/10.1007/978-3-540-45216-4_6
Publication status	Published - 7 Jun 2003
Event	14th Italian Workshop on Neural Nets - , Italy Duration: 4 Jun 2003 → 7 Jun 2003

Publication series

Name	Lecture Notes in Computer Science
Publisher	Springer Verlag
Volume	2859
ISSN (Print)	0302-9743

Conference

Conference	14th Italian Workshop on Neural Nets
Country/Territory	Italy
Period	4/06/03 → 7/06/03

Keywords

probability density function
Pareto front
distribution algorithm
objective function evaluation
Bayesian optimization algorithm

Access to Document

10.1007/978-3-540-45216-4_6

Cite this

Costa, M., Minisci, E., & Pasero, E. (2003). An hybrid neural/genetic approach to continuous multi-objective optimization problems. In B. Apolloni, M. Marinaro, & R. Tagliaferri (Eds.), Neural Nets: 14th Italian Workshop on Neural Nets, WIRN VIETRI 2003, Vietri sul Mare, Italy, June 4-7, 2003. Revised Papers (pp. 61-69). (Lecture Notes in Computer Science; Vol. 2859). Springer. https://doi.org/10.1007/978-3-540-45216-4_6

Costa, Mario ; Minisci, Edmondo ; Pasero, Eros. / An hybrid neural/genetic approach to continuous multi-objective optimization problems. Neural Nets: 14th Italian Workshop on Neural Nets, WIRN VIETRI 2003, Vietri sul Mare, Italy, June 4-7, 2003. Revised Papers. editor / Bruno Apolloni ; Maria Marinaro ; Roberto Tagliaferri. Berlin : Springer, 2003. pp. 61-69 (Lecture Notes in Computer Science).

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abstract = "Evolutionary algorithms perform optimization using the information derived from a population of sample solution points. Recent developments in this field regard optimization as the evolutionary process of an explicit, probabilistic model of the search space. The algorithms derived on the basis of this new philosophy maintain every feature of the classic evolutionary algorithms, but arc able to overcome some drawbacks. In this paper an evolutionary multi-objective optimization tool based on an estimation of distribution algorithm is proposed. It uses the ranking method of non-dominated sorting genetic algorithm-II and the Parzen estimator to approximate the probability density of solutions lying on the Pareto front. The proposed algorithm has been applied to different types of test case problems and results show good performance of the overall optimization procedure in terms of the number of function evaluations.",

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Costa, M, Minisci, E & Pasero, E 2003, An hybrid neural/genetic approach to continuous multi-objective optimization problems. in B Apolloni, M Marinaro & R Tagliaferri (eds), Neural Nets: 14th Italian Workshop on Neural Nets, WIRN VIETRI 2003, Vietri sul Mare, Italy, June 4-7, 2003. Revised Papers. Lecture Notes in Computer Science, vol. 2859, Springer, Berlin, pp. 61-69, 14th Italian Workshop on Neural Nets, Italy, 4/06/03. https://doi.org/10.1007/978-3-540-45216-4_6

An hybrid neural/genetic approach to continuous multi-objective optimization problems. / Costa, Mario; Minisci, Edmondo; Pasero, Eros.
Neural Nets: 14th Italian Workshop on Neural Nets, WIRN VIETRI 2003, Vietri sul Mare, Italy, June 4-7, 2003. Revised Papers. ed. / Bruno Apolloni; Maria Marinaro; Roberto Tagliaferri. Berlin: Springer, 2003. p. 61-69 (Lecture Notes in Computer Science; Vol. 2859).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution book

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N2 - Evolutionary algorithms perform optimization using the information derived from a population of sample solution points. Recent developments in this field regard optimization as the evolutionary process of an explicit, probabilistic model of the search space. The algorithms derived on the basis of this new philosophy maintain every feature of the classic evolutionary algorithms, but arc able to overcome some drawbacks. In this paper an evolutionary multi-objective optimization tool based on an estimation of distribution algorithm is proposed. It uses the ranking method of non-dominated sorting genetic algorithm-II and the Parzen estimator to approximate the probability density of solutions lying on the Pareto front. The proposed algorithm has been applied to different types of test case problems and results show good performance of the overall optimization procedure in terms of the number of function evaluations.

AB - Evolutionary algorithms perform optimization using the information derived from a population of sample solution points. Recent developments in this field regard optimization as the evolutionary process of an explicit, probabilistic model of the search space. The algorithms derived on the basis of this new philosophy maintain every feature of the classic evolutionary algorithms, but arc able to overcome some drawbacks. In this paper an evolutionary multi-objective optimization tool based on an estimation of distribution algorithm is proposed. It uses the ranking method of non-dominated sorting genetic algorithm-II and the Parzen estimator to approximate the probability density of solutions lying on the Pareto front. The proposed algorithm has been applied to different types of test case problems and results show good performance of the overall optimization procedure in terms of the number of function evaluations.

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Costa M, Minisci E, Pasero E. An hybrid neural/genetic approach to continuous multi-objective optimization problems. In Apolloni B, Marinaro M, Tagliaferri R, editors, Neural Nets: 14th Italian Workshop on Neural Nets, WIRN VIETRI 2003, Vietri sul Mare, Italy, June 4-7, 2003. Revised Papers. Berlin: Springer. 2003. p. 61-69. (Lecture Notes in Computer Science). doi: 10.1007/978-3-540-45216-4_6

An hybrid neural/genetic approach to continuous multi-objective optimization problems

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