Genetic programming II: automatic discovery of reusable programs
Genetic programming II: automatic discovery of reusable programs
Discovery of subroutines in genetic programming
Advances in genetic programming
Evaluating GP schema in context
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Using context-aware crossover to improve the performance of GP
Proceedings of the 8th annual conference on Genetic and evolutionary computation
An analysis of diversity of constants of genetic programming
EuroGP'03 Proceedings of the 6th European conference on Genetic programming
A less destructive, context-aware crossover operator for GP
EuroGP'06 Proceedings of the 9th European conference on Genetic Programming
Testing the CAX on a Real-World Problem and Other Benchmarks
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
Semantically driven mutation in genetic programming
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
A non-destructive grammar modification approach to modularity in grammatical evolution
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
Comparing methods for module identification in grammatical evolution
Proceedings of the 14th annual conference on Genetic and evolutionary computation
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This paper introduces a new type of mutation, Context-Aware Mutation, which is inspired by the recently introduced context-aware crossover. Context-Aware mutation operates by replacing existing sub-trees with modules from a previously construct repository of possibly useful sub-trees. We describe an algorithmic way to produce the repository from an initial, exploratory run and test various GP set ups for producing the repository. The results show that when the exploratory run uses context-aware crossover and the main run uses context-aware mutation, not only is the final result significantly better, the overall cost of the runs in terms of individuals evaluated is significantly lower.