Multi-objective probability collectives

Antony Waldock, David Corne

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

4 Citations (Scopus)


We describe and evaluate a multi-objective optimisation (MOO) algorithm that works within the Probability Collectives (PC) optimisation framework. PC is an alternative approach to optimization where the optimization process focusses on finding an ideal distribution over the solution space rather than an ideal solution. We describe one way in which MOO can be done in the PC framework, via using a Pareto-based ranking strategy as a single objective. We partially evaluate this via testing on a number of problems, and compare the results with state of the art alternatives. We find that this first multi-objective probability collectives (MOPC) approach performs competitively, indicating both clear promise, and clear room for improvement. © 2010 Springer-Verlag Berlin Heidelberg.

Original languageEnglish
Title of host publicationApplications of Evolutionary Computation - EvoApplicatons 2010: EvoCOMPLEX, EvoGAMES, EvoIASP, EvoINTELLIGENCE, EvoNUM, and EvoSTOC, Proceedings
Number of pages10
Volume6024 LNCS
EditionPART 1
Publication statusPublished - 2010
EventEvoCOMPLEX, EvoGAMES, EvoIASP, EvoINTELLIGENCE, EvoNUM, and EvoSTOC, EvoApplicatons 2010 - Istanbul, Turkey
Duration: 7 Apr 20109 Apr 2010

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 1
Volume6024 LNCS
ISSN (Print)0302-9743


ConferenceEvoCOMPLEX, EvoGAMES, EvoIASP, EvoINTELLIGENCE, EvoNUM, and EvoSTOC, EvoApplicatons 2010


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