Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Polar IFS+Parisian Genetic Programming=Efficient IFS Inverse Problem Solving
Genetic Programming and Evolvable Machines
Evolving Teams of Predictors with Linear Genetic Programming
Genetic Programming and Evolvable Machines
Inverse Problem Theory and Methods for Model Parameter Estimation
Inverse Problem Theory and Methods for Model Parameter Estimation
Genetic programming with boosting for ambiguities in regression problems
EuroGP'03 Proceedings of the 6th European conference on Genetic programming
Teams of genetic predictors for inverse problem solving
EuroGP'05 Proceedings of the 8th European conference on Genetic Programming
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This paper addresses the resolution, by Genetic Programming (GP) methods, of ambiguous inverse problems, where for a single input, many outputs can be expected. We propose two approaches to tackle this kind of many-to-one inversion problems, each of them based on the estimation, by a team of predictors, of a probability density of the expected outputs. In the first one, Stochastic Realisation GP, the predictors outputs are considered as the realisations of an unknown random variable which distribution should approach the expected one. The second one, Mixture Density GP, directly models the expected distribution by the mean of a Gaussian mixture model, for which genetic programming has to find the parameters. Encouraging results are obtained on four test problems of different difficulty, exhibiting the interests of such methods.