Rostami, S. and Shenfield, A., 2012. CMA-PAES: Pareto archived evolution strategy using covariance matrix adaptation for multi-objective optimisation. In: UKCI 2012: 12th UK Workshop on Computational Intelligence, 5-7 Sep 2012, Edinburgh, UK.
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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.
|Item Type:||Conference or Workshop Item (Paper)|
|Group:||Faculty of Science & Technology|
|Deposited By:||Unnamed user with email symplectic@symplectic|
|Deposited On:||04 Jan 2017 16:00|
|Last Modified:||04 Jan 2017 16:00|
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