Abstract
This paper is concerned with the challenge of learning solutions to problems. The method employed here is a grammar based heuristic, where domain knowledge is encoded in a generative grammar, while evolution drives the update of the population of solutions. Furthermore the method can adapt to the environment by altering the grammar. The implementation consists of the grammar-based Genetic Programming approach of Grammatical Evolution (GE). A number of different constructions of grammars and operators for manipulating the grammars and the evolutionary algorithm are investigated, as well as a meta-grammar GE which allows a more flexible grammar. The results show some benefit of using meta-grammars in GE and re-emphasize the grammar's impact on GE's performance.
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