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• W1509924892 abstract "Inductive learning in First-Order Logic (FOL) is a hard task due to both the prohibitive size of the search space and the computational cost of evaluating hypotheses. This paper introduces an evolutionary algorithm for concept learning in (a fragment of) FOL. The algorithm evolves a population of Horn clauses by repeated selection, mutation and optimization of more fit clauses. Its main novelty, with respect to previous approaches, is the use of stochastic search biases for reducing the complexity of the search process and of the clause fitness evaluation. An experimental evaluation of the algorithm indicates its effectiveness in learning short hypotheses of satisfactory accuracy in a short amount of time." @default.
• W1509924892 created "2016-06-24" @default.
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• W1509924892 date "2002-07-09" @default.
• W1509924892 modified "2023-09-23" @default.
• W1509924892 title "Evolutionary concept learning" @default.
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