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Evolutionary Population Dynamics and Multi-Objective Optimisation Problems

Evolutionary Population Dynamics and Multi-Objective Optimisation Problems
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Author(s): Andrew Lewis (Griffith University, Australia), Sanaz Mostaghim (University of Karlsruhe, Germany) and Marcus Randall (Bond University, Australia)
Copyright: 2008
Pages: 22
Source title: Multi-Objective Optimization in Computational Intelligence: Theory and Practice
Source Author(s)/Editor(s): Lam Thu Bui (University of New South Wales, Australia) and Sameer Alam (University of New South Wales, Australia)
DOI: 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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