Univariate gaussian model for multimodal inseparable problems

Geng Zhang, Yangmin Li, Bingxiao Ding, Yun Li

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

7 Citations (Scopus)


It has been widely perceived that a univariate Gaussian model for evolutionary search can be used to solve separable problems only. This paper explores whether and how the univariate Gaussian model may also be used to solve inseparable problems. The analysis is followed up with experimental tests. The results show that the univariate Gaussian model stipulates no inclination towards separable problems. Further, it is revealed that the model is not only an efficient but also an effective method for solving multimodal inseparable problems. To verify its relative convergence speed, a restart strategy is applied to a univariate Gaussian model (the univariate marginal distribution algorithm) on inseparable problems. The results confirm that the univariate Gaussian model outperforms the five peer algorithms studied in this paper.
Original languageEnglish
Title of host publicationIntelligent Computing Theories and Application - 13th International Conference, ICIC 2017, Proceedings
PublisherSpringer Verlag
Number of pages12
ISBN (Print)9783319633084
Publication statusPublished - 1 Jan 2017
Event13th International Conference on Intelligent Computing, ICIC 2017 - Liverpool, United Kingdom
Duration: 7 Aug 201710 Aug 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10361 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference13th International Conference on Intelligent Computing, ICIC 2017
Country/TerritoryUnited Kingdom


  • Evolutionary computation
  • Inseparable problem
  • Random sampling
  • Univariate model

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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