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

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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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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