Loscos Barroso, Daniel

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First Name
Last Name
Loscos Barroso
Universidad Complutense de Madrid
Faculty / Institute
Sistemas Informáticos y Computación
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Now showing 1 - 3 of 3
  • Publication
    Testing and profiling of regular type operations
    (2020) Loscos Barroso, Daniel; Hermenegildo Salinas, Manuel; López García, Pedro; Morales Caballero, José Francisco
    We performed an audit of the operations of the regular types library included with the Ciao pre-processor, CiaoPP, with the objective of exploring its correctness and efficiency. We centered our investigation on the operations relevant for performing type inference analysis via abstract interpretation, with special attention to the widening operators. We implemented tools to perform our white-box testing of the library, found the bottlenecks for analysis, and proposed some solutions to the main issues diagnosed in the investigation.
  • Publication
    Generalization and Completeness of Evolutionary Computation
    (2018) Loscos Barroso, Daniel; Rodríguez Laguna, Ismael; Martí Oliet, Narciso
    The need of a structured framework for evolutionary computation has been acknowledged. In order to achieve this we designed a set of operational semantics and defined a “general form” of evolutionary computation. Our second approach towards a generalization was to study the relationship between different algorithms and the problems they solve from a performance standpoint. Lastly, we tried to analyze the convergence and complexity of evolutionary algorithms. This led to a set of computability results, the main one being that evolutionary computation is Turing-complete.
  • Publication
    Generalization and completeness of stochastic local search algorithms
    (Elsevier, 2021-09-15) Loscos Barroso, Daniel; Martí Oliet, Narciso; Rodríguez Laguna, Ismael
    We generalize Stochastic Local Search (SLS) heuristics into a unique formal model. This model has two key components: a common structure designed to be as large as possible and a parametric structure intended to be as small as possible. Each heuristic is obtained by instantiating the parametric part in a different way. Particular instances for Genetic Algorithms (GA), Ant Colony Optimization (ACO), and Particle Swarm Optimization (PSO) are presented. Then, we use our model to prove the Turing-completeness of SLS algorithms in general. The proof uses our framework to construct a GA able to simulate any Turing machine. This Turing-completeness implies that determining any non-trivial property concerning the relationship between the inputs and the computed outputs is undecidable for GA and, by extension, for the general set of SLS methods (although not necessarily for each particular method). Similar proofs are more informally presented for PSO and ACO.