Using crossover based similarity measure to improve genetic programming generalization ability
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Definition of a crossover based distance for genetic algorithms
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Theoretical results in genetic programming: the next ten years?
Genetic Programming and Evolvable Machines
Open issues in genetic programming
Genetic Programming and Evolvable Machines
Geometry of evolutionary algorithms
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
How far is it from here to there? a distance that is coherent with GP operators
EuroGP'11 Proceedings of the 14th European conference on Genetic programming
Defining locality as a problem difficulty measure in genetic programming
Genetic Programming and Evolvable Machines
A study of the neutrality of Boolean function landscapes in genetic programming
Theoretical Computer Science
Matrix analysis of genetic programming mutation
EuroGP'12 Proceedings of the 15th European conference on Genetic Programming
An ecological approach to measuring locality in linear genotype to phenotype maps
EuroGP'12 Proceedings of the 15th European conference on Genetic Programming
Geometry of evolutionary algorithms
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
Land cover/land use multiclass classification using GP with geometric semantic operators
EvoApplications'13 Proceedings of the 16th European conference on Applications of Evolutionary Computation
Prediction of forest aboveground biomass: an exercise on avoiding overfitting
EvoApplications'13 Proceedings of the 16th European conference on Applications of Evolutionary Computation
A methodology for user directed search in evolutionary design
Genetic Programming and Evolvable Machines
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In evolutionary algorithms, distance metrics between solutions are often useful for many aspects of guiding and understanding the search process. A good distance measure should reflect the capability of the search: if two solutions are found to be close in distance, or similarity, they should also be close in the search algorithm sense, i.e., the variation operator used to traverse the search space should easily transform one of them into the other. This paper explores such a distance for genetic programming syntax trees. Distance measures are discussed, defined and empirically investigated. The value of such measures is then validated in the context of analysis (fitness-distance correlation is analyzed during population evolution) as well as guiding search (results are improved using our measure in a fitness sharing algorithm) and diversity (new insights are obtained as compared with standard measures).