Evolutionary population dynamics and multi-objective optimisation problems

Andrew Lewis, Sanaz Mostaghim, Marcus Randall

Research output: Chapter in Book/Report/Conference proceedingChapterResearchpeer-review

10 Citations (Scopus)
14 Downloads (Pure)

Abstract

Problems for which many objective functions are to be simultaneously optimised are widely encountered in science and industry. These multi-objective problems have also been the subject of intensive investigation and development recently for metaheuristic search algorithms such as ant colony optimisation, particle swarm optimisation and extremal optimisation. In this chapter, a unifying framework called evolutionary programming dynamics (EPD) is examined. Using underlying concepts of self organised criticality and evolutionary programming, it can be applied to many optimisation algorithms as a controlling metaheuristic, to improve performance and results. We show this to be effective for both continuous and combinatorial problems.

Original languageEnglish
Title of host publicationMulti-Objective Optimization in Computational Intelligence: Theory and Practice
EditorsLam Thu Bui, Sameer Alam
PublisherIGI Global
Pages185-206
Number of pages22
ISBN (Print)9781599044989
DOIs
Publication statusPublished - 2008

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    Lewis, A., Mostaghim, S., & Randall, M. (2008). Evolutionary population dynamics and multi-objective optimisation problems. In L. Thu Bui, & S. Alam (Eds.), Multi-Objective Optimization in Computational Intelligence: Theory and Practice (pp. 185-206). IGI Global. https://doi.org/10.4018/978-1-59904-498-9.ch007