Publication:
Grammatical Evolution Guided by Reinforcement

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2007-09
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IEEE
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Abstract
Grammatical evolution is an evolutionary algorithm able to develop, starting from a grammar, programs in any language. Starting from the point that individual learning can improve evolution, in this paper it is proposed an extension of Grammatical evolution that looks at learning by reinforcement as a learning method for individuals. This way, it is possible to incorporate the Baldwinian mechanism to the evolutionary process. The effect is widened with the introduction of the Lamarck hypothesis. The system is tested in two different domains: a symbolic regression problem and an even parity Boolean function. Results show that for these domains, a system which includes learning obtains better results than a grammatical evolution basic system.
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Congress on Evolutionary Computation. Singapore, 25-28 September 2007
Keywords
Boolean functions, Evolutionary computation, Learning (artificial intelligence), Programming language semantics, Software engineering
Bibliographic citation
IEEE Congress on Evolutionary Computation, 2007. CEC 2007. p. 1475-1482