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Very recently new genetic operators, called geometric semantic operators,have been defined for genetic programming. Contrarily to standard geneticoperators, which are uniquely based on the syntax of the individuals, these newoperators are based on their semantics, meaning with it the set of input-outputpairs on training data. Furthermore, these operators present the interestingproperty of inducing a unimodal fitness landscape for every problem thatconsists in finding a match between given input and output data (for instanceregression and classification). Nevertheless, the current definition of theseoperators has a serious limitation: they impose an exponential growth in thesize of the individuals in the population, so their use is impossible inpractice. This paper is intended to overcome this limitation, presenting a newgenetic programming system that implements geometric semantic operators in anextremely efficient way. To demonstrate the power of the proposed system, weuse it to solve a complex real-life application in the field ofpharmacokinetic: the prediction of the human oral bioavailability of potentialnew drugs. Besides the excellent performances on training data, which wereexpected because the fitness landscape is unimodal, we also report an excellentgeneralization ability of the proposed system, at least for the studiedapplication. In fact, it outperforms standard genetic programming and a wideset of other well-known machine learning methods.

An Efficient Genetic Programming System with Geometric Semantic Operators and its Application to Human Oral Bioavailability Prediction