Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Grammatical Evolution: Evolving Programs for an Arbitrary Language
EuroGP '98 Proceedings of the First European Workshop on Genetic Programming
Grammatical Evolution: Evolutionary Automatic Programming in an Arbitrary Language
Grammatical Evolution: Evolutionary Automatic Programming in an Arbitrary Language
Genetic Programming IV: Routine Human-Competitive Machine Intelligence
Genetic Programming IV: Routine Human-Competitive Machine Intelligence
mGGA: the meta-grammar genetic algorithm
EuroGP'05 Proceedings of the 8th European conference on Genetic Programming
IEEE Transactions on Evolutionary Computation
GEVA: grammatical evolution in Java
ACM SIGEVOlution
Shape grammars and grammatical evolution for evolutionary design
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
An exploration of learning and grammars in grammatical evolution
Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers
Altering search rates of the meta and solution grammars in the mGGA
EuroGP'08 Proceedings of the 11th European conference on Genetic programming
Theoretical results in genetic programming: the next ten years?
Genetic Programming and Evolvable Machines
Grammar-based Genetic Programming: a survey
Genetic Programming and Evolvable Machines
Open issues in genetic programming
Genetic Programming and Evolvable Machines
A non-destructive grammar modification approach to modularity in grammatical evolution
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Tag-based modules in genetic programming
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Exploring grammatical modification with modules in grammatical evolution
EuroGP'11 Proceedings of the 14th European conference on Genetic programming
Unsupervised problem decomposition using genetic programming
EuroGP'10 Proceedings of the 13th European conference on Genetic Programming
Automated problem decomposition for the boolean domain with genetic programming
EuroGP'13 Proceedings of the 16th European conference on Genetic Programming
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The ability of Genetic Programming to scale to problems of increasing difficulty operates on the premise that it is possible to capture regularities that exist in a problem environment by decomposition of the problem into a hierarchy of modules. As computer scientists and more generally as humans we tend to adopt a similar divide-and-conquer strategy in our problem solving. In this paper we consider the adoption of such a strategy for Genetic Algorithms. By adopting a modular representation in a Genetic Algorithm we can make efficiency gains that enable superior scaling characteristics to problems of increasing size. We present a comparison of two modular Genetic Algorithms, one of which is a Grammatical Genetic Programming algorithm, the meta-Grammar Genetic Algorithm (mGGA), which generates binary string sentences instead of traditional GP trees. A number of problems instances are tackled which extend the Checkerboard problem by introducing different kinds of regularity and noise. The results demonstrate some limitations of the modular GA (MGA) representation and how the mGGA can overcome these. The mGGA shows improved scaling when compared the MGA.