Multi-Level Neutrality in Optimization

Johnson, Colin G. (2008) Multi-Level Neutrality in Optimization. In: Proceedings of the 2008 IEEE World Congress on Computational Intelligence, Jun 01-06, 2008, Hong Kong, Peoples R China. (Full text available)

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Abstract

This paper explores the idea of neutrality in heuristic optimization algorithms. In particular, the effect of having multiple levels of neutrality in representations is explored. Two experiments using a fitness-adaptive walk algorithm are carried out: the first is concerned with function optimization with Random Boolean Networks, the second with a tunable neutral mapping applied to the hierarchical if-and-only-if function. In both of these cases it is shown that a two-level neutral mapping can be found that performs better than both nonneutral mappings and mappings with a single level of neutrality.

Item Type:	Conference or workshop item (Paper)
Uncontrolled keywords:	Genetic Algorithms; Neutrality; Hill-Climbing; Optimization
Subjects:	Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming,
Divisions:	Faculties > Sciences > School of Computing > Applied and Interdisciplinary Informatics Group
Depositing User:	Mark Wheadon
Date Deposited:	29 Mar 2010 12:10 UTC
Last Modified:	18 Jan 2017 00:53 UTC
Resource URI:	https://kar.kent.ac.uk/id/eprint/24007 (The current URI for this page, for reference purposes)