Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
A tutorial on support vector regression
Statistics and Computing
Expert Systems with Applications: An International Journal
A distributed PSO-SVM hybrid system with feature selection and parameter optimization
Applied Soft Computing
Support vector machines combined with feature selection for breast cancer diagnosis
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Immune Particle Swarm Optimization for Support Vector Regression on Forest Fire Prediction
ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part II
Expert Systems with Applications: An International Journal
Power load forecasts based on hybrid PSO with Gaussian and adaptive mutation and Wv-SVM
Expert Systems with Applications: An International Journal
Evolutionary tuning of multiple SVM parameters
Neurocomputing
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Evolutionary programming made faster
IEEE Transactions on Evolutionary Computation
Evolutionary programming using mutations based on the Levy probability distribution
IEEE Transactions on Evolutionary Computation
An introduction to simulated evolutionary optimization
IEEE Transactions on Neural Networks
Expert Systems with Applications: An International Journal
Revenue forecasting using a least-squares support vector regression model in a fuzzy environment
Information Sciences: an International Journal
Short-term wind power forecasting using Gaussian processes
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Hi-index | 12.05 |
Hyper-parameters estimation in regression Support Vector Machines (SVMr) is one of the main problems in the application of this type of algorithms to learning problems. This is a hot topic in which very recent approaches have shown very good results in different applications in fields such as bio-medicine, manufacturing, control, etc. Different evolutionary approaches have been tested to be hybridized with SVMr, though the most used are evolutionary approaches for continuous problems, such as evolutionary strategies or particle swarm optimization algorithms. In this paper we discuss the application of two different evolutionary computation techniques to tackle the hyper-parameters estimation problem in SVMrs. Specifically we test an Evolutionary Programming algorithm (EP) and a Particle Swarm Optimization approach (PSO). We focus the paper on the discussion of the application of the complete evolutionary-SVMr algorithm to a real problem of wind speed prediction in wind turbines of a Spanish wind farm.