Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Compositional Evolution: The Impact of Sex, Symbiosis, and Modularity on the Gradualist Framework of Evolution (Vienna Series in Theoretical Biology)
Genetic Programming Crossover: Does It Cross over?
EuroGP '09 Proceedings of the 12th European Conference on Genetic Programming
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
Semantic building blocks in genetic programming
EuroGP'08 Proceedings of the 11th European conference on Genetic programming
Semantically embedded genetic programming: automated design of abstract program representations
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Locally geometric semantic crossover
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
Quantitative analysis of locally geometric semantic crossover
PPSN'12 Proceedings of the 12th international conference on Parallel Problem Solving from Nature - Volume Part I
Genetic Programming and Evolvable Machines
A new implementation of geometric semantic GP and its application to problems in pharmacokinetics
EuroGP'13 Proceedings of the 16th European conference on Genetic Programming
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We propose a class of crossover operators for genetic programming that aim at making offspring programs semantically intermediate (medial) with respect to parent programs by modifying short fragments of code (subprograms). The approach is applicable to problems that define fitness as a distance between program output and the desired output. Based on that metric, we define two measures of semantic ‘mediality', which we employ to design two crossover operators: one aimed at making the semantic of offsprings geometric with respect to the semantic of parents, and the other aimed at making them equidistant to parents' semantics. The operators act only on randomly selected fragments of parents' code, which makes them computationally efficient. When compared experimentally with four other crossover operators, both operators lead to success ratio at least as good as for the non-semantic crossovers, and the operator based on equidistance proves superior to all others.