Robust regression and outlier detection
Robust regression and outlier detection
Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Modeling and analysis of stochastic systems
Modeling and analysis of stochastic systems
Evolution strategies in noisy environments- a survey of existing work
Theoretical aspects of evolutionary computing
Symbolic Regression via Genetic Programming
SBRN '00 Proceedings of the VI Brazilian Symposium on Neural Networks (SBRN'00)
Evaluation of gaussian processes and other methods for non-linear regression
Evaluation of gaussian processes and other methods for non-linear regression
Bounding the effect of noise in multiobjective learning classifier systems
Evolutionary Computation
Mutation and crossover with abstract expression grammars
Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers
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In this paper we propose a genetic programming approach to learning stochastic models with unsymmetrical noise distributions. Most learning algorithms try to learn from noisy data by modeling the maximum likelihood output or least squared error, assuming that noise effects average out. While this process works well for data with symmetrical noise distributions (such as Gaussian observation noise), many real-life sources of noise are not symmetrically distributed, thus this approach does not hold. We suggest improved learning can be obtained by including noise sources explicitly in the model as a stochastic element. A stochastic element is a random sub-process or latent variable of a hidden system that can propagate nonlinear noise to the observable outputs. Stochastic elements can skew and distort output features making regression of analytical models particularly difficult and error minimizing approaches inhibiting. We introduce a new method to infer the analytical model of a system by decomposing non-uniform noise observed at the outputs into uniform stochastic elements appearing symbolically inside the system. Results demonstrate the ability to regress exact analytical models where stochastic elements are embedded inside nonlinear and polynomial hidden systems.