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

Mario Costa, Edmondo Minisci, Eros Pasero

Research output: Chapter in Book/Report/Conference proceedingConference contribution book

3 Citations (Scopus)


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 languageEnglish
Title of host publicationNeural Nets
Subtitle of host publication14th Italian Workshop on Neural Nets, WIRN VIETRI 2003, Vietri sul Mare, Italy, June 4-7, 2003. Revised Papers
EditorsBruno Apolloni, Maria Marinaro, Roberto Tagliaferri
Place of PublicationBerlin
Number of pages9
ISBN (Print)9783540202271, 9783540452164
Publication statusPublished - 7 Jun 2003
Event14th Italian Workshop on Neural Nets - , Italy
Duration: 4 Jun 20037 Jun 2003

Publication series

NameLecture Notes in Computer Science
PublisherSpringer Verlag
ISSN (Print)0302-9743


Conference14th Italian Workshop on Neural Nets


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


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