HMXT-GP: An information-theoretic approach to genetic programming that maintains diversity

Santosa, H; Milton, J; Kennedy, PJ

HMXT-GP: An information-theoretic approach to genetic programming that maintains diversity

Santosa, H Milton, J Kennedy, PJ

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Publication Type:: Conference Proceeding
Citation:: Proceedings of the ACM Symposium on Applied Computing, 2011, pp. 1070 - 1075
Issue Date:: 2011-06-23

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Field	Value	Language
dc.contributor.author	Santosa, H	en_US
dc.contributor.author	Milton, J	en_US
dc.contributor.author	Kennedy, PJ https://orcid.org/0000-0001-7837-3171	en_US
dc.date.issued	2011-06-23	en_US
dc.identifier.citation	Proceedings of the ACM Symposium on Applied Computing, 2011, pp. 1070 - 1075	en_US
dc.identifier.isbn	9781450301138	en_US
dc.identifier.uri	http://hdl.handle.net/10453/19106
dc.description.abstract	This paper applies a recent information - theoretic approach to controlling Genetic Algorithms (GAs) called HMXT to tree - based Genetic Programming (GP). HMXT, in a GA domain, requires the setting of selection thresholds in a population and the application of high levels of crossover to thoroughly mix alleles. Applying these in a tree - based GP setting is not trivial. We present results comparing HMXT - GP to Koza - style GP for varying amounts of crossover and over three different optimisation (minimisation) problems. Results show that average fitness is better with HMXT - GP because it maintains more diversity in populations, but that the minimum fitness found was better with Koza. HMXT allows straightforward tuning of population diversity and selection pressure by altering the position of the selection thresholds. © 2011 ACM.	en_US
dc.relation.ispartof	Proceedings of the ACM Symposium on Applied Computing	en_US
dc.relation.isbasedon	10.1145/1982185.1982420	en_US
dc.title	HMXT-GP: An information-theoretic approach to genetic programming that maintains diversity	en_US
dc.type	Conference Proceeding
utslib.for	080108 Neural, Evolutionary and Fuzzy Computation	en_US
dc.location.activity	Taichung, Taiwan	en_US
dc.location.activity	Niagara Falls, Canada
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
pubs.organisational-group	/University of Technology Sydney/Strength - CHT - Health Technologies
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

This paper applies a recent information - theoretic approach to controlling Genetic Algorithms (GAs) called HMXT to tree - based Genetic Programming (GP). HMXT, in a GA domain, requires the setting of selection thresholds in a population and the application of high levels of crossover to thoroughly mix alleles. Applying these in a tree - based GP setting is not trivial. We present results comparing HMXT - GP to Koza - style GP for varying amounts of crossover and over three different optimisation (minimisation) problems. Results show that average fitness is better with HMXT - GP because it maintains more diversity in populations, but that the minimum fitness found was better with Koza. HMXT allows straightforward tuning of population diversity and selection pressure by altering the position of the selection thresholds. © 2011 ACM.

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http://hdl.handle.net/10453/19106