Genetic programming (videotape): the movie
Genetic programming (videotape): the movie
The Role of Occam‘s Razor in Knowledge Discovery
Data Mining and Knowledge Discovery
An Evaluation of EvolutionaryGeneralisation in Genetic Programming
Artificial Intelligence Review
Fitness Distance Correlation as a Measure of Problem Difficulty for Genetic Algorithms
Proceedings of the 6th International Conference on Genetic Algorithms
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
Maintaining the Diversity of Genetic Programs
EuroGP '02 Proceedings of the 5th European Conference on Genetic Programming
General schema theory for genetic programming with subtree-swapping crossover: part I
Evolutionary Computation
Problem Difficulty and Code Growth in Genetic Programming
Genetic Programming and Evolvable Machines
A Study of Fitness Distance Correlation as a Difficulty Measure in Genetic Programming
Evolutionary Computation
Genetic programming for human oral bioavailability of drugs
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Genetic programming for computational pharmacokinetics in drug discovery and development
Genetic Programming and Evolvable Machines
GECCO '96 Proceedings of the 1st annual conference on Genetic and evolutionary computation
Fitness distance correlation in structural mutation genetic programming
EuroGP'03 Proceedings of the 6th European conference on Genetic programming
Genetic programming, validation sets, and parsimony pressure
EuroGP'06 Proceedings of the 9th European conference on Genetic Programming
Using subtree crossover distance to investigate genetic programming dynamics
EuroGP'06 Proceedings of the 9th European conference on Genetic Programming
Operator-Based distance for genetic programming: subtree crossover distance
EuroGP'05 Proceedings of the 8th European conference on Genetic Programming
Diversity in genetic programming: an analysis of measures and correlation with fitness
IEEE Transactions on Evolutionary Computation
Crossover-Based Tree Distance in Genetic Programming
IEEE Transactions on Evolutionary Computation
Open issues in genetic programming
Genetic Programming and Evolvable Machines
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
Improving the generalisation ability of genetic programming with semantic similarity based crossover
EuroGP'10 Proceedings of the 13th European conference on Genetic Programming
Random sampling technique for overfitting control in genetic programming
EuroGP'12 Proceedings of the 15th European conference on Genetic Programming
Where should we stop? an investigation on early stopping for GP learning
SEAL'12 Proceedings of the 9th international conference on Simulated Evolution and Learning
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Generalization is a very important issue in Machine Learning. In this paper, we present a new idea for improving Genetic Programming generalization ability. The idea is based on a dynamic two-layered selection algorithm and it is tested on a real-life drug discovery regression application. The algorithm begins using root mean squared error as fitness and the usual tournament selection. A list of individuals called ``repulsors'' is also kept in memory and initialized as empty. As an individual is found to overfit the training set, it is inserted into the list of repulsors. When the list of repulsors is not empty, selection becomes a two-layer algorithm: individuals participating to the tournament are not randomly chosen from the population but are themselves selected, using the average dissimilarity to the repulsors as a criterion to be maximized. Two kinds of similarity/dissimilarity measures are tested for this aim: the well known structural (or edit) distance and the recently defined subtree crossover based similarity measure. Although simple, this idea seems to improve Genetic Programming generalization ability and the presented experimental results show that Genetic Programming generalizes better when subtree crossover based similarity measure is used, at least for the test problems studied in this paper.