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

Association for Computing Machinery
Publication Type:
Conference Proceeding
SAC 2011: Proceedings of the 26th Annual ACM Symposium on Applied Computing 2011, 2011, pp. 1070 - 1075
Issue Date:
Full metadata record
Files in This Item:
Filename Description Size
Thumbnail2011001177OK.pdf4.95 MB
Adobe PDF
This paper applies a recent informationtheoretic approach to controlling Genetic Algorithms (GAs) called HMXT to treebased 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 treebased GP setting is not trivial. We present results comparing HMXT GP to Kozastyle GP for varying amounts of crossover and over three different optimisation (minimisation) problems. Results show that average fitness is better with HMXTGP 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.
Please use this identifier to cite or link to this item: