Intensification strategies for extremal optimisation

Marcus Randall*, Andrew Lewis

*Corresponding author for this work

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

4 Citations (Scopus)


It is only relatively recently that extremal optimisation (EO) has been applied to combinatorial optimisation problems. As such, there have been only a few attempts to extend the paradigm to include standard search mechanisms that are routinely used by other techniques such as genetic algorithms, tabu search and ant colony optimisation. The key way to begin this process is to augment EO with attributes that it naturally lacks. While EO does not get confounded by local optima and is able to move through search space unencumbered, one of the major issues is to provide it with better search intensification strategies. In this paper, two strategies that compliment EO's mechanics are introduced and are used to augment an existing solver framework. Results, for single and population versions of the algorithm, demonstrate that intensification aids the performance of EO.

Original languageEnglish
Title of host publicationSimulated Evolution and Learning - 8th International Conference, SEAL 2010, Proceedings
Number of pages10
Volume6457 LNCS
Publication statusPublished - 2010
Event8th International Conference on Simulated Evolution and Learning, SEAL 2010 - Kanpur, India
Duration: 1 Dec 20104 Dec 2010

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume6457 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349


Conference8th International Conference on Simulated Evolution and Learning, SEAL 2010


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