Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Foundations of genetic programming
Foundations of genetic programming
Fitness Distance Correlation as a Measure of Problem Difficulty for Genetic Algorithms
Proceedings of the 6th International Conference on Genetic Algorithms
Genetic programming for computational pharmacokinetics in drug discovery and development
Genetic Programming and Evolvable Machines
Semantic Aware Crossover for Genetic Programming: The Case for Real-Valued Function Regression
EuroGP '09 Proceedings of the 12th European Conference on Genetic Programming
Approximating geometric crossover in semantic space
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Semantic building blocks in genetic programming
EuroGP'08 Proceedings of the 11th European conference on Genetic programming
The role of syntactic and semantic locality of crossover in genetic programming
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part II
Semantically-based crossover in genetic programming: application to real-valued symbolic regression
Genetic Programming and Evolvable Machines
Medial crossovers for genetic programming
EuroGP'12 Proceedings of the 15th European conference on Genetic Programming
Geometric semantic genetic programming
PPSN'12 Proceedings of the 12th international conference on Parallel Problem Solving from Nature - Volume Part I
Land cover/land use multiclass classification using GP with geometric semantic operators
EvoApplications'13 Proceedings of the 16th European conference on Applications of Evolutionary Computation
Prediction of forest aboveground biomass: an exercise on avoiding overfitting
EvoApplications'13 Proceedings of the 16th European conference on Applications of Evolutionary Computation
Expert Systems with Applications: An International Journal
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Moraglio et al. have recently introduced new genetic operators for genetic programming, called geometric semantic operators. These operators induce a unimodal fitness landscape for all the problems consisting in matching input data with known target outputs (like regression and classification). This feature facilitates genetic programming evolvability, which makes these operators extremely promising. Nevertheless, Moraglio et al. leave open problems, the most important one being the fact that these operators, by construction, always produce offspring that are larger than their parents, causing an exponential growth in the size of the individuals, which actually renders them useless in practice. In this paper we overcome this limitation by presenting a new efficient implementation of the geometric semantic operators. This allows us, for the first time, to use them on complex real-life applications, like the two problems in pharmacokinetics that we address here. Our results confirm the excellent evolvability of geometric semantic operators, demonstrated by the good results obtained on training data. Furthermore, we have also achieved a surprisingly good generalization ability, a fact that can be explained considering some properties of geometric semantic operators, which makes them even more appealing than before.