Double space based multiobjective evolutionary algorithm

Junchi Liang, Jia You, Guoqiang Han, Le Li

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review


Recently, solving multiobjective problems are gaining more and more attention due to its useful applications in the area of engineering, bioinformatics, pattern recognition. Although there exist a lot of multiobjective evolutionary algorithms (MOEAs) for solving multiobjective problems, few of them considers the evolutionary process in both the solution space and the objective space. In the paper, we will propose a new hybrid multiobjective evolutionary algorithm named as double space based multiobjective evolutionary algorithms (DS-MOEA) to perform multiobjective optimization. Compared with traditional MOEAs, DS-MOEA not only considers the evolutionary process in the solution space, but also takes into account the knowledge learning process in the objective space. The results in the experiment illustrate that DS-MOEA works well during the process of solving multiobjective problems.
Original languageEnglish
Title of host publicationProceedings of 2012 International Conference on Machine Learning and Cybernetics, ICMLC 2012
Number of pages6
Publication statusPublished - 31 Dec 2012
Event2012 International Conference on Machine Learning and Cybernetics, ICMLC 2012 - Xian, Shaanxi, China
Duration: 15 Jul 201217 Jul 2012


Conference2012 International Conference on Machine Learning and Cybernetics, ICMLC 2012
CityXian, Shaanxi


  • Evolutionary algorithm
  • Multiobjective optimization
  • Objective space

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computational Theory and Mathematics
  • Computer Networks and Communications
  • Human-Computer Interaction


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