J
Jose A. Lozano
Researcher at Basque Center for Applied Mathematics
Publications - 351
Citations - 15977
Jose A. Lozano is an academic researcher from Basque Center for Applied Mathematics. The author has contributed to research in topics: Estimation of distribution algorithm & EDAS. The author has an hindex of 48, co-authored 321 publications receiving 13725 citations. Previous affiliations of Jose A. Lozano include University of California, Los Angeles & University of Essex.
Papers
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Book
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
Pedro Larraanaga,Jose A. Lozano +1 more
TL;DR: This book presents an introduction to Evolutionary Algorithms, a meta-language for programming with real-time implications, and some examples of how different types of algorithms can be tuned for different levels of integration.
Journal ArticleDOI
Sensitivity Analysis of k-Fold Cross Validation in Prediction Error Estimation
TL;DR: This paper analyzes the statistical properties, bias and variance, of the k-fold cross-validation classification error estimator (k-cv) and proposes a novel theoretical decomposition of the variance considering its sources of variance: sensitivity to changes in the training set and sensitivity to changed folds.
BookDOI
Estimation of Distribution Algorithms
Pedro Larrañaga,Jose A. Lozano +1 more
TL;DR: This work approaches the problem of partial abductive inference in Bayesian networks by means of Estimation of Distribution Algorithms, and an empirical comparison between the results obtained by Genetic Algorithm and Estimating of DistributionAlgorithms is carried out.
BookDOI
Parallel Problem Solving from Nature - PPSN VIII
Xin Yao,Edmund K. Burke,Jose A. Lozano,James C. Smith,Juan J. Merelo-Guervós,John A. Bullinaria,Jonathan E. Rowe,Peter Tiňo,Ata Kabán,Hans-Paul Schwefel +9 more
Journal ArticleDOI
An empirical comparison of four initialization methods for the K-Means algorithm
TL;DR: The results suggest that the Kaufman initialization method induces to the K-Means algorithm a more desirable behaviour with respect to the convergence speed than the random initialization method.