Modern mathematical statistics
Modern mathematical statistics
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
Machine Learning
Evolutionary Algorithms in Management Applications
Evolutionary Algorithms in Management Applications
Evolutionary Algorithms: The Role of Mutation and Recombination
Evolutionary Algorithms: The Role of Mutation and Recombination
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Evolutionary strategies for multi-scale radial basis function kernels in support vector machines
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
The Genetic Kernel Support Vector Machine: Description and Evaluation
Artificial Intelligence Review
On the effect of populations in evolutionary multi-objective optimization
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Learning process models with missing data
ECML'06 Proceedings of the 17th European conference on Machine Learning
ECML'06 Proceedings of the 17th European conference on Machine Learning
A hybrid GA/SVM approach for gene selection and classification of microarray data
EuroGP'06 Proceedings of the 2006 international conference on Applications of Evolutionary Computing
Evolutionary programming made faster
IEEE Transactions on Evolutionary Computation
A robust stochastic genetic algorithm (StGA) for global numerical optimization
IEEE Transactions on Evolutionary Computation
Local function approximation in evolutionary algorithms for the optimization of costly functions
IEEE Transactions on Evolutionary Computation
Evolutionary optimization in uncertain environments-a survey
IEEE Transactions on Evolutionary Computation
A general noise model and its effects on evolution strategy performance
IEEE Transactions on Evolutionary Computation
Efficient search for robust solutions by means of evolutionary algorithms and fitness approximation
IEEE Transactions on Evolutionary Computation
Computational intelligence in astronomy --- a win-win situation
TPNC'12 Proceedings of the First international conference on Theory and Practice of Natural Computing
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We study the problem of estimating the time delay between two signals representing delayed, irregularly sampled and noisy versions of the same underlying pattern. We propose and demonstrate an evolutionary algorithm for the (hyper)parameter estimation of a kernel-based technique in the context of an astronomical problem, namely estimating the time delay between two gravitationally lensed signals from a distant quasar. Mixed types (integer and real) are used to represent variables within the evolutionary algorithm. We test the algorithm on several artificial data sets, and also on real astronomical observations of quasar Q0957+561. By carrying out a statistical analysis of the results we present a detailed comparison of our method with the most popular methods for time delay estimation in astrophysics. Our method yields more accurate and more stable time delay estimates. Our methodology can be readily applied to current state-of-the-art optical monitoring data in astronomy, but can also be applied in other disciplines involving similar time series data.