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There is great interest for the development of semantic genetic operators to improve the performance of genetic programming. Semantic genetic operators have traditionally been developed employing experimentally or theoretically-based approaches. Our current work proposes a novel semantic crossover developed amid the two traditional approaches. Our proposed semantic crossover operator is based on the use of the derivative of the error propagated through the tree. This process decides the crossing point of the second parent.

Sa`ent, vol. 1 (1768) 12. : Measuring bloat, overfitting and functional complexity in genetic programming. In: Proceedings of the 12th Annual Conference on Genetic and Evolutionary Computation, pp. 877–884. ACM (2010) 13. : Open issues in genetic programming. Genetic Programming and Evolvable Machines 11(3-4), 339–363 (2010) 14. : Contribution to the theory of periodic reactions. The Journal of Physical Chemistry 14(3), 271–274 (1910) 15. : A growth cycle. In: Socialism, Capitalism and Economic Growth, pp.

2021 which 46 M. Graff, A. Graff-Guerrero, and J. 0464). Unfortunately, it is not available the lengths of the tree generated by SIHC and consequently the full performance cannot be compared. 4 Conclusions Our work presents the development of a novel semantic crossover operator that is based on the derivative of the errors. The results showed that choosing the crossover point of the second parent according to Equation 1 improves considerably the learning abilities of the GP systems. GPPDE showed the best performance in almost all the problems tested (1095 out of 1100), and it also obtained on average the best performance in comparison to the systems presented here, and, also, to the 20 systems previously presented by our group [18].

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Access 2007 by John Hales

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