A genetic system for learning models of consumer choice
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Competition-Based Induction of Decision Models from Examples
Machine Learning - Special issue on genetic algorithms
Classifier Systems and the Animat Problem
Machine Learning
Machine Learning
A Tale of Two Classifier Systems
Machine Learning
An Investigation of Niche and Species Formation in Genetic Function Optimization
Proceedings of the 3rd International Conference on Genetic Algorithms
Biases in the Crossover Landscape
Proceedings of the 3rd International Conference on Genetic Algorithms
Triggered Rule Discovery in Classifier Systems
Proceedings of the 3rd International Conference on Genetic Algorithms
Uniform Crossover in Genetic Algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
An analysis of the behavior of a class of genetic adaptive systems.
An analysis of the behavior of a class of genetic adaptive systems.
Intelligent behavior as an adaptation to the task environment
Intelligent behavior as an adaptation to the task environment
Learning disjunction of conjunctions
IJCAI'85 Proceedings of the 9th international joint conference on Artificial intelligence - Volume 1
Learning concept classification rules using genetic algorithms
IJCAI'91 Proceedings of the 12th international joint conference on Artificial intelligence - Volume 2
A Learning Classifier Systems Bibliography
Learning Classifier Systems, From Foundations to Applications
Learning Classifier Systems, From Foundations to Applications
A Bigger Learning Classifier Systems Bibliography
IWLCS '00 Revised Papers from the Third International Workshop on Advances in Learning Classifier Systems
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Promoting and maintaining diversity is a critical requirement of search in learning classifier systems (LCSs). What is required of the genetic algorithm (GA) in an LCS context is not convergence to a single global maximum, as in the standard optimization framework, but instead the generation of individuals (i.e., rules) that collectively cover the overall problem space. COGIN (COverage-based Genetic INduction) is a system designed to exploit genetic recombination for the purpose of constructing rule-based classification models from examples. The distinguishing characteristic of COGIN is its use of coverage of training set examples as an explicit constraint on the search, which acts to promote appropriate diversity in the population of rules over time. By treating training examples as limited resources, COGIN creates an ecological model that simultaneously accommodates a dynamic range of niches while encouraging superior individuals within a niche, leading to concise and accurate decision models. Previous experimental studies with COGIN have demonstrated its performance advantages over several well-known symbolic induction approaches. In this paper, we examine the effects of two modifications to the original system configuration, each designed to inject additional diversity into the search: increasing the carrying capacity of training set examples (i.e., increasing coverage redundancy) and increasing the level of disruption in the recombination operator used to generate new rules. Experimental results are given that show both types of modifications to yield substantial improvements to previously published results.