Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Competition-Based Induction of Decision Models from Examples
Machine Learning - Special issue on genetic algorithms
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Learning Sequential Decision Rules Using Simulation Models and Competition
Machine Learning - Special issue on genetic algorithms
Classifier Systems and the Animat Problem
Machine Learning
Machine Learning
Triggered Rule Discovery in Classifier Systems
Proceedings of the 3rd International Conference on Genetic Algorithms
Flexible learning of problem solving heuristics through adaptive search
IJCAI'83 Proceedings of the Eighth international joint conference on Artificial intelligence - Volume 1
Constructive induction on decision trees
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 1
Concept learning and the problem of small disjuncts
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 1
Generating production rules from decision trees
IJCAI'87 Proceedings of the 10th 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
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COGIN is a system designed for induction of symbolic decision models from pre-classed examples based on the use of genetic algorithms (GAs). Much research in symbolic induction has focused on techniques for reducing classification inaccuracies that arise from inherent limits of underlying incremental search techniques. Genetic Algorithms offer an intriguing alternative to stepwise model construction, relying instead on model evolution through global competition. The difficulty is in providing an effective framework for the GA to be practically applied to complex induction problems. COGIN merges traditional induction concepts with genetic search to provide such a framework, and recent experimental results have demonstrated its advantage relative to basic stepwise inductive approaches. In this paper, we describe the essential elements of the COGIN approach and present a favorable comparison of COGIN results with those produced by a more sophisticated stepwise approach (with support post processing) on standardized multiplexor problems.