Multi-objective phylogenetic algorithm: Solving multi-objective decomposable deceptive problems

Jean Paulo Martins, Antonio Helson Mineiro Soares, Danilo Vasconcellos Vargas, Alexandre Cláudio Botazzo Delbem

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

5 Citations (Scopus)

Abstract

In general, Multi-objective Evolutionary Algorithms do not guarantee find solutions in the Pareto-optimal set. We propose a new approach for solving decomposable deceptive multi-objective problems that can find all solutions of the Pareto-optimal set. Basically, the proposed approach starts by decomposing the problem into subproblems and, then, combining the found solutions. The resultant approach is a Multi-objective Estimation of Distribution Algorithm for solving relatively complex multi-objective decomposable problems, using a probabilistic model based on a phylogenetic tree. The results show that, for the tested problem, the algorithm can efficiently find all the solutions of the Pareto-optimal set, with better scaling than the hierarchical Bayesian Optimization Algorithm and other algorithms of the state of art.

Original languageEnglish
Title of host publicationEvolutionary Multi-Criterion Optimization - 6th International Conference, EMO 2011, Proceedings
Pages285-297
Number of pages13
DOIs
Publication statusPublished - 2011
Externally publishedYes
Event6th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2011 - Ouro Preto, Brazil
Duration: Apr 5 2011Apr 8 2011

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume6576 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other6th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2011
Country/TerritoryBrazil
CityOuro Preto
Period4/5/114/8/11

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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