- author = "Henry Santosa and John Milton and Paul J. Kennedy",
- title = "{HMXT-GP}: an information-theoretic approach to genetic programming that maintains diversity",
- booktitle = "Proceedings of the 2011 ACM Symposium on Applied Computing",
- year = "2011",
- editor = "William C. Chu and W. Eric Wong and Mathew J. Palakal and Chih-Cheng Hung",
- pages = "1070--1075",
- address = "TaiChung, Taiwan",
- month = "21-25 " # mar,
- publisher = "ACM",
- keywords = "genetic algorithms, genetic programming, HMXT, ECJ, diversity, evolutionary computation, information theory",
- acmid = "1982420",
- isbn13 = "978-1-4503-0113-8",
-
URL = "
http://hdl.handle.net/10453/19106",
-
DOI = "
doi:10.1145/1982185.1982420",
- size = "6 pages",
- 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.",
-
notes = "cart, binomial-3, Mario AI.
Mutation as memoryless information source.
p1073 'adding diversity as in HMXT-GP hinders convergence between the multiple (competing) solutions.'
HMXT ECJ GA paper p234 says 'The minimum amount of crossover that achieves this return [after selection] to a binomial distribution is referred to as sufficient crossover'.
SAC'11 University of Technology, Sydney, Broadway NSW, Australia",
