On the Application of Hierarchical Coevolutionary Genetic Algorithms

Journal of Applied System Sciences, 4(2), pp 2-17, 2003

15 Pages Posted: 17 Aug 2016

See all articles by Uwe Aickelin

Uwe Aickelin

University of Melbourne - School of Computing and Information Systems

Larry Bull

University of the West of England (UWE)

Date Written: January 1, 2003

Abstract

This paper examines the use of a hierarchical coevolutionary genetic algorithm under different partnering strategies. Cascading clusters of sub-populations are built from the bottom up, with higher-level sub-populations optimising larger parts of the problem. Hence higher-level sub-populations potentially search a larger search space with a lower resolution whilst lower-level sub-populations search a smaller search space with a higher resolution. The effects of different partner selection schemes amongst the sub-populations on solution quality are examined for two constrained optimisation problems. We examine a number of recombination partnering strategies in the construction of higher-level individuals and a number of related schemes for evaluating sub-solutions. It is shown that partnering strategies that exploit problemspecific knowledge are superior and can counter inappropriate (sub-) fitness measurements.

Keywords: Genetic Algorithms, Coevolution, Scheduling

Suggested Citation

Aickelin, Uwe and Bull, Larry, On the Application of Hierarchical Coevolutionary Genetic Algorithms (January 1, 2003). Journal of Applied System Sciences, 4(2), pp 2-17, 2003, Available at SSRN: https://ssrn.com/abstract=2824161

Uwe Aickelin (Contact Author)

University of Melbourne - School of Computing and Information Systems ( email )

Australia

Larry Bull

University of the West of England (UWE)

Blackberry Hill Bristol
West Bristol
Bristol, Avon BS16 1QY
United Kingdom

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