@inproceedings{f7bff4d23ecf42dd894355284eb101d3,
title = "Biasing evolving generations in learning classifier systems using information theoretic measures",
abstract = "This paper presents information-theoretic ideas to modify the course of evolution in Learning Classifier Systems. This approach exploits the possibilities that individuals in each generation contain potentially useful information that is not currently utilized. In particular, we look at the Sufficiency measure of a rule as an information theoretic indicator. We propose the modification of the XCS algorithm using this in the early formative stages of each run in view of these additional indicators of usefulness. The probability of selection during that period would be based on sufficiency. Preliminary simulation results show that the new approach reduces the effort needed to solve the 20-input multiplexer problem.",
keywords = "biased evolution, genetic algorithms, information theory, learning classifier systems, sufficiency, xcs",
author = "Karthik Kuber and Mohan, {Chilukuri K.}",
note = "Publisher Copyright: {\textcopyright} 2009 Copyright is held by the author/owner(s).; 11th Annual Genetic and Evolutionary Computation Conference, GECCO-2009 ; Conference date: 08-07-2009 Through 12-07-2009",
year = "2009",
doi = "10.1145/1570256.1570279",
language = "English (US)",
isbn = "9781605583259",
series = "Proceedings of the 11th Annual Genetic and Evolutionary Computation Conference, GECCO-2009",
publisher = "Association for Computing Machinery",
pages = "2077--2080",
booktitle = "Proceedings of the 11th Annual Genetic and Evolutionary Computation Conference, GECCO-2009",
}