Genetic programming II: automatic discovery of reusable programs
Genetic programming II: automatic discovery of reusable programs
The sciences of the artificial (3rd ed.)
The sciences of the artificial (3rd ed.)
Hierarchical learning with procedural abstraction mechanisms
Hierarchical learning with procedural abstraction mechanisms
Discovery of subroutines in genetic programming
Advances in genetic programming
Genetic Programming and Autoconstructive Evolution with the Push Programming Language
Genetic Programming and Evolvable Machines
Grammatical Evolution: Evolutionary Automatic Programming in an Arbitrary Language
Grammatical Evolution: Evolutionary Automatic Programming in an Arbitrary Language
Architecture-Altering Operations for Evolving the Architecture of a Multi-Part Program in Genetic Programming
Context-aware mutation: a modular, context aware mutation operator for genetic programming
Proceedings of the 9th annual conference on Genetic and evolutionary computation
GEVA: grammatical evolution in Java
ACM SIGEVOlution
Functional modularity for genetic programming
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Foundations in Grammatical Evolution for Dynamic Environments
Foundations in Grammatical Evolution for Dynamic Environments
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
The Automatic Acquisition, Evolution and Reuse of Modules in Cartesian Genetic Programming
IEEE Transactions on Evolutionary Computation
Analyzing module usage in grammatical evolution
PPSN'12 Proceedings of the 12th international conference on Parallel Problem Solving from Nature - Volume Part I
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Modularity has been an important vein of research in evolutionary algorithms. Past research in evolutionary computation has shown that techniques able to decompose the benchmark problems examined in this work into smaller, more easily solved, sub-problems have an advantage over those which do not. This work describes and analyzes a number of approaches to discover sub-solutions (modules) in the grammatical evolution algorithm. Data from the experiments carried out show that particular approaches to identifying modules are better suited to certain problem types, at varying levels of difficulty. The results presented here show that some of these approaches are able to significantly outperform standard grammatical evolution and grammatical evolution using automatically defined functions on a subset of the problems tested. The results also point to a number of possibilities for extending this work to further enhance approaches to modularity.