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  • An adaptive neuroevolution-basedhyperheuristic 

    Etor A.; Ceberio J.; Pérez A.; Irurozki E. (The Genetic and Evolutionary Computation Conference, 2020-07-08)
    According to the No-Free-Lunch theorem, an algorithm that performs efficiently on any type of problem does not exist. In this sense, algorithms that exploit problem-specific knowledge usually outperform more generic ...
  • Optimization of deep learning precipitation models using categorical binary metrics 

    Larraondo P.R.; Renzullo L.J.; Van Dijk A.I.J.M.; Inza I.; Lozano J.A. (Journal of Advances in Modeling Earth Systems, 2020)
    This work introduces a methodology for optimizing neural network models using a combination of continuous and categorical binary indices in the context of precipitation forecasting. Probability of detection or false alarm ...
  • An efficient K-means clustering algorithm for tall data 

    Capo M.; Pérez A.; Lozano J.A. (DATA MINING AND KNOWLEDGE DISCOVERY, 2020)
    The analysis of continously larger datasets is a task of major importance in a wide variety of scientific fields. Therefore, the development of efficient and parallel algorithms to perform such an analysis is a a crucial ...
  • Supervised non-parametric discretization based on Kernel density estimation 

    Flores J. L.; Calvo B.; Pérez A. (Pattern Recognition Letters, 2019-12-19)
    Nowadays, machine learning algorithms can be found in many applications where the classifiers play a key role. In this context, discretizing continuous attributes is a common step previous to classification tasks, the main ...
  • On-line Elastic Similarity Measures for time series 

    Oregui I.; Pérez A.; Del Ser J.; Lozano J.A. (Pattern Recognition, 2019-04)
    The way similarity is measured among time series is of paramount importance in many data mining and machine learning tasks. For instance, Elastic Similarity Measures are widely used to determine whether two time series are ...
  • An Experimental Study in Adaptive Kernel Selection for Bayesian Optimization 

    Roman I.; Santana R.; Mendiburu A.; Lozano J.A. (IEEE Access, 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 ...
  • Hybrid Heuristics for the Linear Ordering Problem 

    Garcia E.; Ceberio J.; Lozano J.A. (2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings, 2019)
    The linear ordering problem (LOP) is one of the classical NP-Hard combinatorial optimization problems. Motivated by the difficulty of solving it up to optimality, in recent decades a great number of heuristic and meta-heuristic ...
  • Soft information for localization-of-things 

    Conti A.; Mazuelas S.; Bartoletti S.; Lindsey W.C; Win M. (Proceeding of the IEEE, 2019-11-01)
    Location awareness is vital for emerging Internetof- Things applications and opens a new era for Localizationof- Things. This paper first reviews the classical localization techniques based on single-value metrics, such ...
  • Data generation approaches for topic classification in multilingual spoken dialog systems 

    Montenegro C.; Santana R.; Lozano J.A. (ACM International Conference Proceeding Series, 2019)
    The conception of spoken-dialog systems (SDS) usually faces the problem of extending or adapting the system to multiple languages. This implies the creation of modules specically for the new languages, which is a time ...
  • Anatomy of the attraction basins: Breaking with the intuition 

    Hernando L.; Mendiburu A.; Lozano J.A. (Evolutionary Computation, 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 ...
  • Characterising the rankings produced by combinatorial optimisation problems and finding their intersections 

    Hernando L.; Mendiburu A.; Lozano J.A. (GECCO '19 Proceedings of the Genetic and Evolutionary Computation Conference, 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 ...
  • Approaching the Quadratic Assignment Problem with Kernels of Mallows Models under the Hamming Distance 

    Arza E.; Ceberio J.; Pérez A.; Irurozki E. (Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2019-07)
    The Quadratic Assignment Problem (QAP) is a specially challenging permutation-based np-hard combinatorial optimization problem, since instances of size $n>40$ are seldom solved using exact methods. In this sense, many ...
  • Sentiment analysis with genetically evolved Gaussian kernels 

    Roman I.; Santana R.; Mendiburu A.; Lozano J.A. (GECCO '19 Proceedings of the Genetic and Evolutionary Computation Conference, 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. ...
  • Optimal multi-impulse space rendezvous considering limited impulse using a discretized Lambert problem combined with evolutionary algorithms 

    Shirazi A.; Ceberio J.; Lozano J.A. (8th European Conference for Aeronautics and Space Sciences, 2019-07-01)
    In this paper, a direct approach is presented to tackle the multi-impulse rendezvous problem considering the impulse limit. Particularly, the standard Lambert problem is extended toward several consequential orbit transfers ...
  • An evolutionary discretized Lambert approach for optimal long-range rendezvous considering impulse limit 

    Shirazi A.; Ceberio J.; Lozano J.A. (Aerospace Science and Technology, 2019-09-18)
    In this paper, an approach is presented for finding the optimal long-range space rendezvous in terms of fuel and time, considering limited impulse. In this approach , the Lambert problem is expanded towards a discretized ...
  • Analyzing rare event, anomaly, novelty and outlier detection terms under the supervised classification framework 

    Carreño A.; Inza I.; Lozano J.A. (Artificial Intelligence Review, 2019-09-01)
    In recent years, a variety of research areas have contributed to a set of related problems with rare event, anomaly, novelty and outlier detection terms as the main actors. These multiple research areas have created a ...
  • Crowd-Centric Counting via Unsupervised Learning 

    Morselli F.; Bartoletti S.; Mazuelas S.; Win M.; Conti A. (2019 IEEE International Conference on Communications Workshops (ICC Workshops), 2019-07-11)
    Counting targets (people or things) within a moni-tored area is an important task in emerging wireless applications,including those for smart environments, safety, and security.Conventional device-free radio-based ...
  • A mathematical analysis of edas with distance-based exponential models 

    Unanue I.; Merino M.; Lozano J.A. (GECCO '19 Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2019-07-01)
    Estimation of Distribution Algorithms have been successfully used for solving many combinatorial optimization problems. One type of problems in which Estimation of Distribution Algorithms have presented strong competitive ...
  • Belief Condensation Filtering For Rssi-Based State Estimation In Indoor Localization 

    Mehryary S.; Mazuelas S.; Malekzadehz P.; Spachos P.; Plataniotisy K.N.; Mohammadi A. (2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2019)
    Recent advancements in signal processing and communication systems have resulted in evolution of an intriguing concept referred to as Internet of Things (IoT). By embracing the IoT evolution, there has been a surge of ...
  • On the evaluation and selection of classifier learning algorithms with crowdsourced data 

    Urkullu A.; Perez A.; Calvo B. (Applied Soft Computing, 2019-02-16)
    In many current problems, the actual class of the instances, the ground truth, is unavail- able. Instead, with the intention of learning a model, the labels can be crowdsourced by harvesting them from different annotators. ...

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