DICE: A new family of bivariate estimation of distribution algorithms based on dichotomised multivariate gaussian distributions

Fergal Lane*, R. Muhammad Atif Azad, Conor Ryan

*Corresponding author for this work

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    2 Citations (Scopus)
    Original languageEnglish
    Title of host publicationApplications of Evolutionary Computation - 20th European Conference, EvoApplications 2017, Proceedings
    EditorsJ.Ignacio Hidalgo, Carlos Cotta, Ting Hu, Alberto Tonda, Paolo Burrelli, Matt Coler, Giovanni Iacca, Michael Kampouridis, Antonio M. Mora Garcia, Giovanni Squillero, Anthony Brabazon, Evert Haasdijk, Jacqueline Heinerman, Fabio D Andreagiovanni, Jaume Bacardit, Trung Thanh Nguyen, Sara Silva, Ernesto Tarantino, Anna I. Esparcia-Alcazar, Gerd Ascheid, Kyrre Glette, Stefano Cagnoni, Paul Kaufmann, Francisco Fernandez de Vega, Michalis Mavrovouniotis, Mengjie Zhang, Federico Divina, Kevin Sim, Neil Urquhart, Robert Schaefer
    PublisherSpringer Verlag
    Pages670-685
    Number of pages16
    ISBN (Print)9783319558486
    DOIs
    Publication statusPublished (VoR) - 2017
    Event20th European Conference on the Applications of Evolutionary Computation, EvoApplications 2017 - Amsterdam, Netherlands
    Duration: 19 Apr 201721 Apr 2017

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume10199 LNCS
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349

    Conference

    Conference20th European Conference on the Applications of Evolutionary Computation, EvoApplications 2017
    Country/TerritoryNetherlands
    City Amsterdam
    Period19/04/1721/04/17

    Keywords

    • Combinatorial optimization
    • Dichotomised Gaussian models
    • EDAs

    Fingerprint

    Dive into the research topics of 'DICE: A new family of bivariate estimation of distribution algorithms based on dichotomised multivariate gaussian distributions'. Together they form a unique fingerprint.

    Cite this