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
An Evaluation of EvolutionaryGeneralisation in Genetic Programming
Artificial Intelligence Review
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
Genetic programming for human oral bioavailability of drugs
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Genetic Programming and Evolvable Machines
A Statistical Learning Perspective of Genetic Programming
EuroGP '09 Proceedings of the 12th European Conference on Genetic Programming
Operator equalisation, bloat and overfitting: a study on human oral bioavailability prediction
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
GECCO '96 Proceedings of the 1st annual conference on Genetic and evolutionary computation
Generality versus size in genetic programming
GECCO '96 Proceedings of the 1st annual conference on Genetic and evolutionary computation
IEEE Transactions on Evolutionary Computation
Measuring and Optimizing Behavioral Complexity for Evolutionary Reinforcement Learning
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part II
Using Operator Equalisation for Prediction of Drug Toxicity with Genetic Programming
EPIA '09 Proceedings of the 14th Portuguese Conference on Artificial Intelligence: Progress in Artificial Intelligence
A Field Guide to Genetic Programming
A Field Guide to Genetic Programming
Measuring bloat, overfitting and functional complexity in genetic programming
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Open issues in genetic programming
Genetic Programming and Evolvable Machines
Genetic programming, validation sets, and parsimony pressure
EuroGP'06 Proceedings of the 9th European conference on Genetic Programming
A bootstrapping approach to reduce over-fitting in genetic programming
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
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The relationship between generalization and solutions functional complexity in genetic programming (GP) has been recently investigated. Three main contributions are contained in this paper: (1) a new measure of functional complexity for GP solutions, called Graph Based Complexity (GBC) is defined and we show that it has a higher correlation with GP performance on out-of-sample data than another complexity measure introduced in a recent publication. (2) A new measure is presented, called Graph Based Learning Ability (GBLA). It is inspired by the GBC and its goal is to quantify the ability of GP to learn "difficult" training points; we show that GBLA is negatively correlated with the performance of GP on out-of-sample data. (3) Finally, we use the ideas that have inspired the definition of GBC and GBLA to define a new fitness function, whose suitability is empirically demonstrated. The experimental results reported in this paper have been obtained using three real-life multidimensional regression problems.