Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Effects of locality in individual and population evolution
Advances in genetic programming
Alignment of trees: an alternative to tree edit
Theoretical Computer Science
An introduction to Kolmogorov complexity and its applications (2nd ed.)
An introduction to Kolmogorov complexity and its applications (2nd ed.)
Foundations of genetic programming
Foundations of genetic programming
Evolution strategies –A comprehensive introduction
Natural Computing: an international journal
Finding needles in haystacks is harder with neutrality
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
A Study of Fitness Distance Correlation as a Difficulty Measure in Genetic Programming
Evolutionary Computation
Representations for Genetic and Evolutionary Algorithms
Representations for Genetic and Evolutionary Algorithms
Sustaining diversity using behavioral information distance
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
On the bias and performance of the edge-set encoding
IEEE Transactions on Evolutionary Computation
A Field Guide to Genetic Programming
A Field Guide to Genetic Programming
On the locality of grammatical evolution
EuroGP'06 Proceedings of the 9th European conference on Genetic Programming
IEEE Transactions on Information Theory
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
Defining locality as a problem difficulty measure in genetic programming
Genetic Programming and Evolvable Machines
Exploration and exploitation in evolutionary algorithms: A survey
ACM Computing Surveys (CSUR)
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Locality - how well neighbouring genotypes correspond to neighbouring phenotypes - has been defined as a key element affecting how Evolutionary Computation systems explore and exploit the search space. Locality has been studied empirically using the typical Genetic Algorithm (GA) representation (i.e., bitstrings), and it has been argued that locality plays an important role in EC performance. To our knowledge, there are few explicit studies of locality using the typical Genetic Programming (GP) representation (i.e., tree-like structures). The aim of this paper is to address this important research gap. We extend the genotype-phenotype definition of locality to GP by studying the relationship between genotypes and fitness. We consider a mutation-based GP system applied to two problems which are highly difficult to solve by GP (a multimodal deceptive landscape and a highly neutral landscape). To analyse in detail the locality in these instances, we adopt three popular mutation operators. We analyse the operators' genotypic step sizes in terms of three distance measures taken from the specialised literature and in terms of corresponding fitness values. We also analyse the frequencies of different sizes of fitness change.