Evolving programmers: the co-evolution of intelligent recombination operators
Advances in genetic programming
Genetic programming: an introduction: on the automatic evolution of computer programs and its applications
Machine Learning
On Comparing Classifiers: Pitfalls toAvoid and a Recommended Approach
Data Mining and Knowledge Discovery
Genetic Programming and Evolvable Machines
Review: Discipulus: A Commercial Genetic Programming System
Genetic Programming and Evolvable Machines
Complexity Compression and Evolution
Proceedings of the 6th International Conference on Genetic Algorithms
Size fair and homologous tree crossovers
Size fair and homologous tree crossovers
Genetic Programming and Evolvable Machines
Robustness and evolvability of recombination in linear genetic programming
EuroGP'13 Proceedings of the 16th European conference on Genetic Programming
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While successful applications have been reported using standard GP crossover, limitations of this approach have been identified by several investigators. Among the most compelling alternatives to standard GP crossover are those that use some form of homologous crossover, where code segments that are exchanged are structurally or syntactically aligned in order to preserve context and worth. This paper reports the results of an empirical comparison of GP using standard crossover methods with GP using homologous crossover methods. Ten problems are tested, five each of pattern recognition and regression.Results suggest that in terms of generalization accuracy, homologous crossover does generate consistently better performance. In addition, there is a consistently lower fraction of introns that are generated in the solution code.