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Evolutionary Population Dynamics and Multi-Objective Optimisation Problems

Evolutionary Population Dynamics and Multi-Objective Optimisation Problems

Andrew Lewis, Sanaz Mostaghim, Marcus Randall
ISBN13: 9781599044989|ISBN10: 1599044986|EISBN13: 9781599045009
DOI: 10.4018/978-1-59904-498-9.ch007
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MLA

Lewis, Andrew, et al. "Evolutionary Population Dynamics and Multi-Objective Optimisation Problems." Multi-Objective Optimization in Computational Intelligence: Theory and Practice, edited by Lam Thu Bui and Sameer Alam, IGI Global, 2008, pp. 185-206. https://doi.org/10.4018/978-1-59904-498-9.ch007

APA

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

Chicago

Lewis, Andrew, Sanaz Mostaghim, and Marcus Randall. "Evolutionary Population Dynamics and Multi-Objective Optimisation Problems." In Multi-Objective Optimization in Computational Intelligence: Theory and Practice, edited by Lam Thu Bui and Sameer Alam, 185-206. Hershey, PA: IGI Global, 2008. https://doi.org/10.4018/978-1-59904-498-9.ch007

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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.

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