Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
A Metric for Genetic Programs and Fitness Sharing
Proceedings of the European Conference on Genetic Programming
Explicit Control of Diversity and Effective Variation Distance in Linear Genetic Programming
EuroGP '02 Proceedings of the 5th European Conference on Genetic Programming
A Study of Fitness Distance Correlation as a Difficulty Measure in Genetic Programming
Evolutionary Computation
Semantic Aware Crossover for Genetic Programming: The Case for Real-Valued Function Regression
EuroGP '09 Proceedings of the 12th European Conference on Genetic Programming
Approximating geometric crossover in semantic space
Proceedings of the 11th Annual conference on Genetic and 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
Proceedings of the 11th workshop proceedings on Foundations of genetic algorithms
Learning stochastic tree edit distance
ECML'06 Proceedings of the 17th European conference on Machine Learning
Crossover-Based Tree Distance in Genetic Programming
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Information Theory
A distance between populations for one-point crossover in genetic algorithms
Theoretical Computer Science
A methodology for user directed search in evolutionary design
Genetic Programming and Evolvable Machines
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The distance between pairs of individuals is a useful concept in the study of evolutionary algorithms. It is particularly useful to define a distance which is coherent with, i.e. related to, the action of a particular operator. We present the first formal, general definition of this operator-distance coherence. We also propose a new distance function, based on the multi-step transition probability (MSTP), that is coherent with any GP operator for which the one-step transition probability (1STP) between individuals can be defined. We give an algorithm for 1STP in the case of subtree mutation. Because MSTP is useful in GP investigations, but impractical to compute, we evaluate a variety of means to approximate it. We show that some syntactic distance measures give good approximations, and attempt to combine them to improve the approximation using a GP symbolic regression method. We conclude that 1STP itself is a sufficient indicator of MSTP for subtree mutation.