Geometric Semantic Genetic Programming for Real Life Applications

In a recent contribution we have introduced a new implementation of geometric semantic operators for Genetic Programming. Thanks to this implementation, we are now able to deeply investigate their usefulness and study their properties on complex real-life applications. Our experiments confirm that these operators are more effective than traditional ones in optimizing training data, due to the fact that they induce a unimodal fitness landscape. Furthermore, they automatically limit overfitting, something we had already noticed in our recent contribution, and that is further discussed here. Finally, we investigate the influence of some parameters on the effectiveness of these operators, and we show that tuning their values and setting them “a priori” may be wasted effort. Instead, if we randomly modify the values of those parameters several times during the evolution, we obtain a performance that is comparable with the one obtained with the best setting, both on training and test data for all the studied problems.