Grammatical Evolution: Evolutionary Automatic Programming in an Arbitrary Language
Grammatical Evolution: Evolutionary Automatic Programming in an Arbitrary Language
Meta-grammar constant creation with grammatical evolution by grammatical evolution
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Biologically Inspired Algorithms for Financial Modelling (Natural Computing Series)
Biologically Inspired Algorithms for Financial Modelling (Natural Computing Series)
A grammatical genetic programming approach to modularity in genetic algorithms
EuroGP'07 Proceedings of the 10th European conference on Genetic programming
An attribute grammar decoder for the 01 multiconstrained knapsack problem
EvoCOP'05 Proceedings of the 5th European conference on Evolutionary Computation in Combinatorial Optimization
mGGA: the meta-grammar genetic algorithm
EuroGP'05 Proceedings of the 8th European conference on Genetic Programming
Shape grammars and grammatical evolution for evolutionary design
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
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Adopting ameta-Grammar with Grammatical Evolution(GE) allows GE to evolve the grammar that it uses to specify the construction of a syntactically correct solution. The ability to evolve a grammar in the context of GE means that useful bias towards specific structures and solutions can be evolved during a run. This can lead to improved performance over the standard static grammar in terms of adapting to a dynamic environment and improved scalability to larger problem instances. This approach allows the evolution of modularity and reuse both on structural and symbol levels resulting in a compression of the representation of a solution. In this paper an analysis of altering the rate of sampling of the evolved solution grammars is undertaken. It is found that themajority of evolutionary search is currently focused on the generation of the solution grammars to such an extent that the candidate solutions are often hard-coded into them making the solution chromosome effectively redundant. This opens the door to future work in which we can explore how the search can be better balanced between the meta and solution grammars.