CMA-PAES: Pareto archived evolution strategy using covariance matrix adaptation for multi-objective optimisation

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Authors: Rostami, S. and Shenfield, A.

http://eprints.bournemouth.ac.uk/25958/

Journal: 2012 12th UK Workshop on Computational Intelligence, UKCI 2012

ISBN: 9781467343923

DOI: 10.1109/UKCI.2012.6335782

The quality of Evolutionary Multi-Objective Optimisation (EMO) approximation sets can be measured by their proximity, diversity and pertinence. In this paper we introduce a modular and extensible Multi-Objective Evolutionary Algorithm (MOEA) capable of converging to the Pareto-optimal front in a minimal number of function evaluations and producing a diverse approximation set. This algorithm, called the Covariance Matrix Adaptation Pareto Archived Evolution Strategy (CMA-PAES), is a form of (μ + λ) Evolution Strategy which uses an online archive of previously found Pareto-optimal solutions (maintained by a bounded Pareto-archiving scheme) as well as a population of solutions which are subjected to variation using Covariance Matrix Adaptation. The performance of CMA-PAES is compared to NSGA-II (currently considered the benchmark MOEA in the literature) on the ZDT test suite of bi-objective optimisation problems and the significance of the results are analysed using randomisation testing. © 2012 IEEE.

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