Efficiently representing populations in genetic programming
Advances in genetic programming
Genetic Algorithms
Fitness Distance Correlation as a Measure of Problem Difficulty for Genetic Algorithms
Proceedings of the 6th International Conference on Genetic Algorithms
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
Linear Genetic Programming (Genetic and Evolutionary Computation)
Linear Genetic Programming (Genetic and Evolutionary Computation)
Representations for Genetic and Evolutionary Algorithms
Representations for Genetic and Evolutionary Algorithms
Representation and structural biases in CGP
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
A fine-grained view of GP locality with binary decision diagrams as ant phenotypes
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part I
Semantically embedded genetic programming: automated design of abstract program representations
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Defining locality as a problem difficulty measure in genetic programming
Genetic Programming and Evolvable Machines
On the locality of grammatical evolution
EuroGP'06 Proceedings of the 9th European conference on Genetic Programming
An analysis of genotype-phenotype maps in grammatical evolution
EuroGP'10 Proceedings of the 13th European conference on Genetic Programming
Crossover-Based Tree Distance in Genetic Programming
IEEE Transactions on Evolutionary Computation
Improving Locality in Binary Representation via Redundancy
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Semantic bias in program coevolution
EuroGP'13 Proceedings of the 16th European conference on Genetic Programming
GECCO 2013 tutorial: cartesian genetic programming
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
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Recent research has considered the role of locality in GP representations. We use a modified statistical technique drawn from numerical ecology, the Mantel test, to measure the locality of integer-encoded GP. Weak locality is identified in a case study on Cartesian Genetic Programming (CGP), a directed acyclic graph representation. A method of varying syntactic program locality continuously through the application of a biased mutation operator is demonstrated. The impact of varying locality under the new measure is assessed over a randomly generated set of polynomial symbolic regression problems. We observe that enforcing higher levels of locality in CGP is associated with poorer performance on the problem set and discuss implications in the context of existing models of GP genotype-phenotype maps.