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Journey to the center of the linear ordering problem
Hernando, L.; Mendiburu, A.; Lozano, J.A.
(2020-06)
(2020-06)
A number of local search based algorithms have been designed to escape from the local optima, such as, iterated local search or variable neighborhood search. The neighborhood chosen for the local search as well as the ...
in-depth analysis of SVM kernel learning and its components
Roman, I.; Santana, R.; Mendiburu, A.; Lozano, J.A. (2020)
(2020)
The performance of support vector machines in non-linearly-separable classification problems strongly relies on the kernel function. Towards an automatic machine learning approach for this technique, many research outputs ...
Early classification of time series using multi-objective optimization techniques
Mori, U.; Mendiburu, A.; Miranda, I.M.; Lozano, J.A. (2019-04-23)
(2019-04-23)
In early classification of time series the objective is to build models which are able to make class-predictions for time series as accurately and as early as possible, when only a part of the series is available. It is ...
Sentiment analysis with genetically evolved Gaussian kernels
Roman, I.; Santana, R.; Mendiburu, A.; Lozano, J.A. (2019)
(2019)
Sentiment analysis consists of evaluating opinions or statements based on text analysis. Among the methods used to estimate the degree to which a text expresses a certain sentiment are those based on Gaussian Processes. ...
Characterising the rankings produced by combinatorial optimisation problems and finding their intersections
Hernando, L.; Mendiburu, A.; Lozano, J.A. (2019)
(2019)
The aim of this paper is to introduce the concept of intersection between combinatorial optimisation problems. We take into account that most algorithms, in their machinery, do not consider the exact objective function ...
An Experimental Study in Adaptive Kernel Selection for Bayesian Optimization
Roman, I.; Santana, R.; Mendiburu, A.; Lozano, J.A. (2019)
(2019)
Bayesian Optimization has been widely used along with Gaussian Processes for solving expensive-to-evaluate black-box optimization problems. Overall, this approach has shown good results, and particularly for parameter ...
Evolving Gaussian Process Kernels for Translation Editing Effort Estimation
Roman, I.; Santana, R.; Mendiburu, A.; Lozano, J.A. (2019)
(2019)
In many Natural Language Processing problems the combination of machine learning and optimization techniques is essential. One of these problems is estimating the effort required to improve, under direct human supervision, ...
Bayesian Optimization Approaches for Massively Multi-modal Problems
Roman, I.; Mendiburu, A.; Santana, R.; Lozano, J.A. (2019)
(2019)
The optimization of massively multi-modal functions is a challenging task, particularly for problems where the search space can lead the op- timization process to local optima. While evolutionary algorithms have been ...
Anatomy of the attraction basins: Breaking with the intuition
Hernando, L.; Mendiburu, A.; Lozano, J.A. (2019)
(2019)
olving combinatorial optimization problems efficiently requires the development of algorithms that consider the specific properties of the problems. In this sense, local search algorithms are designed over a neighborhood ...
Distance-based exponential probability models on constrained combinatorial optimization problems
Ceberio, J.; Mendiburu, A.; Lozano, J.A. (2018-08-30)
(2018-08-30)
Estimation of distribution algorithms have already demonstrated their utility when solving a broad range of combinatorial problems. However, there is still room for methodological improvements when approaching constrained ...