The role of crossover and mutation in genetic programming has been the subject of much debate since the emergence of the field. Recently new genetic operators, called geometric semantic operators, have been introduced. Contrary to standard genetic operators, these operators present the interesting property of inducing a unimodal fitness landscape for every problem that consists in finding a match between inputs and targets. As the definition of these operators is quite recent, their effect on the evolutionary dynamics is still in many senses unknown and deserves to be studied. This paper intends to fill this gap, with a specific focus on applications in the field of pharmacokinetic. Results show that a mixture of semantic crossover and mutation is always beneficial compared to the use of only one of these operators. Furthermore, we show that the best results are obtained using values of the semantic mutation rate which are considerably higher than the ones that are typically used when traditional subtree mutation is employed.
- Genetic programming
- Geometric operators