Grammatical Evolution: Evolutionary Automatic Programming in an Arbitrary Language
Grammatical Evolution: Evolutionary Automatic Programming in an Arbitrary Language
Grammatical bias for evolutionary learning
Grammatical bias for evolutionary learning
GEVA: grammatical evolution in Java
ACM SIGEVOlution
Foundations in Grammatical Evolution for Dynamic Environments
Foundations in Grammatical Evolution for Dynamic Environments
A grammatical genetic programming approach to modularity in genetic algorithms
EuroGP'07 Proceedings of the 10th European conference on Genetic programming
A Field Guide to Genetic Programming
A Field Guide to Genetic Programming
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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.