Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Maintaining the Diversity of Genetic Programs
EuroGP '02 Proceedings of the 5th European Conference on Genetic Programming
Using subtree crossover distance to investigate genetic programming dynamics
EuroGP'06 Proceedings of the 9th European conference on Genetic Programming
Operator-Based distance for genetic programming: subtree crossover distance
EuroGP'05 Proceedings of the 8th European conference on Genetic Programming
Crossover-Based Tree Distance in Genetic Programming
IEEE Transactions on Evolutionary Computation
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Distances that are bound to (or consistent with) genetic operators are measures that quantify the difficulty of reaching and individual (or a population) starting from another individual (or population) and applying the genetic operator iteratively. Defining distance measures bound to genetic operators is a very important task in evolutionary computation. In fact these distances usually make the analysis of some indicators of the the search process, like for instance population diversity or well-known measures of problem hardness such as fitness distance correlation, more accurate. In this paper, we introduce a distance measure bound to one point standard crossover for genetic algorithms. This measure quantifies the minimum number of crossover operations that have to be applied to a population to tranform it into another population. It is based on the definition of a lattice over some particular schemata that represent the individuals in the population and on the construction of a discrete dynamic system that models the dynamics of the genetic algorithm under the sole effect of crossover. Using this distance measure, it is also possible to build a family of distances between individuals.