Carlos Artemio Coello Coello

Group Leader. Ikerbasque Research Professor

T +34 946 567 842
F +34 946 567 842

Information of interest

Group Leader at BCAM. 

  • Revisiting Implicit and Explicit Averaging for Noisy Optimization 

    Ahrari, A.; Elsayed, S.; Sarker, R.; Essam, D.; Coello, C.A.Autoridad BCAM (2023-10-01)
    Explicit and implicit averaging are two well-known strategies for noisy optimization. Both strategies can counteract the disruptive effect of noise; however, a critical question remains: which one is more efficient? This ...
  • Challenging test problems for multi- and many-objective optimization 

    Zapotecas-Martínez, S.; Coello, C.A.Autoridad BCAM; Aguirre, H.E.; Tanaka, K. (2023-08-01)
    In spite of the extensive studies that have been conducted regarding the construction of multi-objective test problems, researchers have mainly focused their interests on designing complicated search spaces, disregarding, ...
  • Discretization-Based Feature Selection as a Bilevel Optimization Problem 

    Rihab, S.; Maha, E.; Bechikh, S.; Coello, C.A.Autoridad BCAM; Said, L. B. (2023-08-01)
    Discretization-based feature selection (DBFS) approaches have shown interesting results when using several metaheuristic algorithms, such as particle swarm optimization (PSO), genetic algorithm (GA), ant colony optimization ...
  • On the Construction of Pareto-Compliant Combined Indicators 

    Falcón-Cardona, J.G.; Emmerich, M.; Coello, C.A.Autoridad BCAM (2022-08-12)
    The most relevant property that a quality indicator (QI) is expected to have is Pareto compliance, which means that every time an approximation set strictly dominates another in a Pareto sense, the indicator must reflect ...
  • Multiple source transfer learning for dynamic multiobjective optimization 

    Ye, Y.; Lin, Q.; Ma, L.; Wong, K. C.; Gong, M.; Coello, C.A.Autoridad BCAM (2022-08-01)
    Recently, dynamic multiobjective evolutionary algorithms (DMOEAs) with transfer learning have become popular for solving dynamic multiobjective optimization problems (DMOPs), as the used transfer learning methods in DMOEAs ...
  • Parallel Multi-Objective Evolutionary Algorithms: A Comprehensive Survey 

    Falcón-Cardona, J.G.; Hernández Gómez, R.; Coello, C.A.Autoridad BCAM; Castillo Tapia, M.G. (2021-12-01)
    Multi-Objective Evolutionary Algorithms (MOEAs) are powerful search techniques that have been extensively used to solve difficult problems in a wide variety of disciplines. However, they can be very demanding in terms of ...
  • A Novel Parametric benchmark generator for dynamic multimodal optimization 

    Ahrari, A.; Elsayed, S.; Sarker, R.; Essam, D.; Coello, C.A.Autoridad BCAM (2021-08-01)
    In most existing studies on dynamic multimodal optimization (DMMO), numerical simulations have been performed using the Moving Peaks Benchmark (MPB), which is a two-decade-old test suite that cannot simulate some critical ...

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