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  • Design of Loss Functions for Solving Inverse Problems using Deep Learning 

    Rivera J.A.; Pardo D.; Alberdi E. (Computational Science – ICCS 2020, 2020-05)
    Solving inverse problems is a crucial task in several applications that strongly a ffect our daily lives, including multiple engineering fields, military operations, and/or energy production. There exist different methods ...
  • Kernels of Mallows Models under the Hamming Distance for solving the Quadratic Assignment Problem 

    Arza E.; Pérez A.; Irurozki E.; Ceberio J. (Swarm and Evolutionary Computation, 2020-07)
    The Quadratic Assignment Problem (QAP) is a well-known permutation-based combinatorial optimization problem with real applications in industrial and logistics environments. Motivated by the challenge that this NP-hard ...
  • An adaptive neuroevolution-based hyperheuristic 

    Arza E.; Ceberio J.; Pérez A.; Irurozki E. (The Genetic and Evolutionary Computation Conference, 2020)
    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 ...
  • Evolving Gaussian Process Kernels for Translation Editing Effort Estimation 

    Roman I.; Santana R.; Mendiburu A.; Lozano J.A. (Learning and Intelligent Optimization, 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. (Learning and Intelligent Optimization, 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 ...

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