Recent Submissions

  • Distance-based exponential probability models on constrained combinatorial optimization problems 

    Ceberio J.; Mendiburu A.; Lozano, J.A. (GECCO 2018 Companion - Proceedings of the 2018 Genetic and Evolutionary Computation Conference Companion, 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 ...
  • Bayesian inference for algorithm ranking analysis 

    Calvo B.; Ceberio J.; Lozano J.A. (GECCO 2018 Companion - Proceedings of the 2018 Genetic and Evolutionary Computation Conference Companion 6 July 2018, Pages 324-325, 2018-08-30)
    The statistical assessment of the empirical comparison of algorithms is an essential step in heuristic optimization. Classically, researchers have relied on the use of statistical tests. However, recently, concerns about ...
  • Spacecraft Trajectory Optimization: A review of Models, Objectives, Approaches and Solutions 

    Shirazi A.; Ceberio J.; Lozano J.A. (Progress in Aerospace Sciences, 2018)
    This article is a survey paper on solving spacecraft trajectory optimization problems. The solving process is decomposed into four key steps of mathematical modeling of the problem, defining the objective functions, ...
  • A note on the behavior of majority voting in multi-class domains with biased annotators 

    Hernández-González J.; Inza I.; Lozano J.A. (IEEE Transactions on Knowledge and Data Engineering, 2018-05)
    Majority voting is a popular and robust strategy to aggregate different opinions in learning from crowds, where each worker labels examples ac- cording to their own criteria. Although it has been extensively studied in the ...
  • Effects of reducing VMs management times on elastic applications 

    Pascual J.A.; Lozano J.A.; Miguel-Alonso J. (Journal of Grid Computing, 2018-05)
    Cloud infrastructures provide computing resources to applications in the form of Virtual Machines (VMs). Many applications deployed in cloud resources have an elastic behavior, that is, they change the number of servers ...
  • On-Line Dynamic Time Warping for Streaming Time Series 

    Oregi I.; Pérez A.; Del Ser J.; Lozano J.A. (Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2017. Lecture Notes in Computer Science, 2017-09)
    Dynamic Time Warping is a well-known measure of dissimilarity between time series. Due to its flexibility to deal with non-linear distortions along the time axis, this measure has been widely utilized in machine learning ...
  • Are the artificially generated instances uniform in terms of difficulty? 

    Pérez A.; Ceberio J.; Lozano J.A. (IEEE Congress on Evolutionary Computation, 2018-06)
    In the field of evolutionary computation, it is usual to generate artificial benchmarks of instances that are used as a test-bed to determine the performance of the algorithms at hand. In this context, a recent work on ...
  • Nature-inspired approaches for distance metric learning in multivariate time series classification 

    Oregui I.; Del Ser J.; Pérez A.; Lozano J.A. (IEEE Congress on Evolutionary Computation (CEC), 2017-07)
    The applicability of time series data mining in many different fields has motivated the scientific community to focus on the development of new methods towards improving the performance of the classifiers over this particular ...
  • An efficient approximation to the K-means clustering for Massive Data 

    Capo M.; Pérez A.; Lozano J.A. (Knowledge-Based Systems, 2017-02-01)
    Due to the progressive growth of the amount of data available in a wide variety of scientific fields, it has become more difficult to manipulate and analyze such information. In spite of its dependency on the initial ...
  • Efficient approximation of probability distributions with k-order decomposable models 

    Pérez A.; Inza I.; Lozano J.A. (International Journal of Approximate Reasoning, 2016-07)
    During the last decades several learning algorithms have been proposed to learn probability distributions based on decomposable models. Some of these algorithms can be used to search for a maximum likelihood decomposable ...
  • Soft range information for network localization 

    Mazuelas S.; Conti A.; Allen J.C.; Win M.Z. (IEEE Transactions on Signal Processing, 2018-06-15)
    The demand for accurate localization in complex environments continues to increase despite the difficulty in extracting positional information from measurements. Conventional range-based localization approaches rely on ...
  • Multi-objectivising Combinatorial Optimisation Problems by means of Elementary Landscape Decompositions 

    Ceberio J.; Calvo B.; Mendiburu A.; Lozano JA. (Evolutionary Computation, 2017-12)
    In the last decade, many works in combinatorial optimisation have shown that, due to the advances in multi-objective optimisation, the algorithms from this field could be used for solving single-objective problems as well. ...
  • A system for airport weather forecasting based on circular regression trees 

    Rozas-Larrondo P.; Inza I.; Lozano J.A. (Environmental Modelling & Software, 2017-11-01)
    This paper describes a suite of tools and a model for improving the accuracy of airport weather forecasts produced by numerical weather prediction (NWP) products, by learning from the relationships between previously ...
  • Learning to classify software defects from crowds: a novel approach 

    Hernández-González J.; Rodríguez D.; Inza I.; Rachel H.; Lozano J.A. (Applied Soft Computing, 2017-11-01)
    In software engineering, associating each reported defect with a cate- gory allows, among many other things, for the appropriate allocation of resources. Although this classification task can be automated using stan- dard ...
  • Early classification of time series by simultaneously optimizing the accuracy and earliness 

    Mori U.; Mendiburu A.; Dasgupta S.; Lozano J.A. (IEEE Transactions on Neural Networks and Learning Systems, 2017-10)
    The problem of early classi cation of time series appears naturally in contexts where the data, of temporal nature, is collected over time, and early class predictions are interesting or even required. The objective is to ...
  • An efficient evolutionary algorithm for the orienteering problem 

    Kobeaga G.; Merino M.; Lozano J.A. (Computers and Operations Research, 2017-09-06)
    This paper deals with the Orienteering Problem, which is a routing problem. In the Orienteering Problem, each node has a profit assigned and the goal is to find the route that maximizes the total collected profit subject ...
  • The Weighted Independent Domination Problem: ILP Model and Algorithmic Approaches 

    Pinacho Davidson P.; Blum C.; Lozano J.A. (European Journal of Operational Research, 2017-08-30)
    This work deals with the so-called weighted independent domination problem, which is an $NP$-hard combinatorial optimization problem in graphs. In contrast to previous work, this paper considers the problem from a ...
  • Measuring the Class-imbalance Extent of Multi-class Problems 

    Ortigosa-Hernández J.; Inza I.; Lozano J.A. (Pattern Recognition Letter, 2017-07-30)
    Since many important real-world classification problems involve learning from unbalanced data, the challenging class-imbalance problem has lately received con- siderable attention in the community. Most of the methodological ...
  • The weighted independent domination problem: ILP model and algorithmic approaches 

    Davidson P.P.; Blum C.; Lozano J.A. (Lecture Notes in Computer Science, 2017-06-01)
    This work deals with the so-called weighted independent domination problem, which is an N P -hard combinatorial optimization problem in graphs. In contrast to previous theoretical work from the liter- ature, this paper ...
  • Evolutionary algorithms to optimize low-thrust trajectory design in spacecraft orbital precession mission 

    Shirazi A.; Ceberio J.; Lozano J.A. (IEEE Congress on Evolutionary Computation, 2017-06-06)
    In space environment, perturbations make the spacecraft lose its predefined orbit in space. One of these undesirable changes is the in-plane rotation of space orbit, denominated as orbital precession. To overcome this ...

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