Numerical Recipes in C++: the art of scientific computing
Numerical Recipes in C++: the art of scientific computing
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
On the effect of populations in evolutionary multi-objective optimization
Proceedings of the 8th annual conference on Genetic and evolutionary computation
ECML'06 Proceedings of the 17th European conference on Machine Learning
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An evolutionary algorithm for parameter estimation of a kernel method for noisy and irregularly sampled time series is presented. We aim to estimate the time delay between time series coming from gravitational lensing in astronomy. The parameters to estimate include the delay, the width of kernels or smoothing, and a regularization parameter. We use mixed types to represent variables within the evolutionary algorithm. The algorithm is tested on several artificial data sets, and also on real astronomical observations. The performance of our method is compared with the most popular methods for time delay estimation. An statistical analysis of results is given, where the results of our approach are more accurate and significant than those of other methods.