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Genetics-Based Machine Learning systems suffer from many problems as representational weaknesses. We propose to introduce more general structures we used to learn disjunctive normal forms. Results show how our model can be used to discover and maintain complete classifier solutions.

Learning disjunctive normal forms in a dual classifier system (Extended abstract)