An Evolutionary Approach to Concept Learning with Structured Data
This paper details the implementation of a strongly-typed evolutionary programming system (STEPS) and its application to concept learning from highly-structured examples. STEPS evolves concept descriptions in the form of program trees. Predictive accuracy is used as the fitness function to be optimised through genetic operations. Empirical results with representative applications demonstrate promise.
KeywordsGenetic Programming Crossover Point Concept Learning Inductive Logic Inductive Logic Programming
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