Memetic Genetic Programming based on orthogonal projections in the phenotype space

Genetic Programming (GP) is an evolutionary algorithm that has received a lot of attention lately due to its success in solving hard real-world problems. Lately, there has been a great interest in GP's community to develop semantic genetic operators, i.e., operators that work on the phenotype. In this contribution, we improve the performance of GP by making orthogonal projections in the phenotype space using the behavior of the parents and the target, i.e., the problem at hand. The technique proposed can be easily applied to any tree based GP, and, as the result show this technique statistically improves the performance of GP. Furthermore, we experimentally show how a traditional GP system enhanced with our technique can outperform the state of the art geometric semantic GP systems.