Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Simulation and the Monte Carlo Method (Wiley Series in Probability and Statistics)
Simulation and the Monte Carlo Method (Wiley Series in Probability and Statistics)
Genetic Programming and Evolvable Machines
Operator equalisation, bloat and overfitting: a study on human oral bioavailability prediction
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
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Recent bloat control methods such as dynamic depth limit (DynLimit) and Dynamic Operator Equalization (DynOpEq) aim at modifying the tree size distribution in a population of genetic programs. Although they are quite efficient for that purpose, these techniques have the disadvantage of evaluating the fitness of many bloated Genetic Programming (GP) trees, and then rejecting most of them, leading to an important waste of computational resources. We are proposing a method that makes a histogram-based model of current GP tree size distribution, and uses the so-called accept-reject method for generating a population with the desired target size distribution, in order to make a stochastic control of bloat in the course of the evolution. Experimental results show that the method is able to control bloat as well as other state-of-the-art methods, with minimal additionnal computational efforts compared to standard tree-based GP.