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

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.

Full text available as:

[img]
Preview
PDF
06335782.pdf - Accepted Version
Available under License Creative Commons Attribution Non-commercial No Derivatives.

310kB

Official URL: http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp...

DOI: 10.1109/UKCI.2012.6335782

Abstract

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)
Subjects:UNSPECIFIED
Group:Faculty of Science & Technology
ID Code:25958
Deposited By: Unnamed user with email symplectic@symplectic
Deposited On:04 Jan 2017 16:00
Last Modified:04 Jan 2017 16:00

Downloads

Downloads per month over past year

More statistics for this item...
Repository Staff Only -