Examinando por Autor "Norero, E."

Mostrando 1 - 3 de 3
  • Miniatura
    ÍtemAcceso Abierto
    A binary coded firefly algorithm that solves the set covering problem
    (Editura Academiei Romane, 2014) Crawford, B.; Soto, R.; Olivares-Suárez, M.; Palma, W.; Paredes, F.; Olguín, E.; Norero, E.
    This work presents a study of a new binary coded firefly algorithm. The firefly algorithm is a novel nature-inspired metaheuristic, inspired by the social behavior of fireflies, which is being applied to solve many optimization problems. We test the proposed binary coded firefly algorithm solving the non-unicost set covering problem which is a well-known NP-hard discrete optimization problem with many practical applications. To tackle the mapping from a continuous search space to a discrete search space we use different transfer functions which are investigated in terms of convergence speed and accuracy of results. The experimental results show the effectiveness of our approach where the binary coded firefly algorithm produce competitive results solving a portfolio of set covering problems from the OR-Library.
  • Miniatura
    ÍtemAcceso Abierto
    Towards a framework for adaptive constraint propagation
    (Springer Verlag, 2015) Crawford, B.; Soto, R.; Johnson, F.; Monfroy, E.; Norero, E.; Olguín, E.
    In this paper we address a recent situation created by the explosive growth of web systems. For these reason we propose a framework to support adaptive elements in Web pages. Web pages can be accessed by different platforms with different browsers and through different devices such as laptops, tablets or cellphones. In particular we focus on adaptive menus for this different kind of devices or browsers to optimize the selection patterns and their implementations. We propose a framework using an Adaptive Constraint Programming technique to optimize the decision of developers. Constraint Programming is a programming paradigm able to find efficient resolution in optimization problems. In Constraint Programming a problem is defined in term of variables and constraints. The variables hold a domain and represent the unknowns of the problem, while the relations among them are modeled as constraints. © Springer International Publishing Switzerland 2015.
  • Miniatura
    ÍtemAcceso Abierto
    Using autonomous search for solving constraint satisfaction problems via new modern approaches
    (Elsevier, 2016) Soto, R.; Crawford, B.; Olivares, R.; Galleguillos, C.; Castro, C.; Johnson, F.; Paredes, F.; Norero, E.
    Constraint Programming is a powerful paradigm which allows the resolution of many complex problems, such as scheduling, planning, and configuration. These problems are defined by a set of variables and a set of constraints. Each variable has non-empty domain of possible value and each constraint involves some subset of the variables and specifies the allowable combinations of values for that subset. The resolution of these problems is carried out by a constraint satisfaction solver which explores a search tree of potential solutions. This exploration is controlled by the enumeration strategy, which is responsible for choosing the order in which variables and values are selected to generate the potential solution. There exist different ways to perform this selection, and depending on the quality of this decision, the efficiency of the solving process may dramatically vary. Autonomous search is a particular case of adaptive systems that aims at improving its solving performance by adapting itself to the problem at hand without manual configuration of an expert user. The goal is to improve their solving performance by modifying and adjusting themselves, either by self-adaptation or by supervised adaptation. This approach has been effectively applied to different optimization and satisfaction techniques such as constraint programming, metaheuristics, and SAT. In this paper, we present a new Autonomous Search approach for constraint programming based on four modern bio-inspired metaheuristics. The goal of those metaheuristics is to optimize the self-tuning phase of the constraint programming search process. We illustrate promising results, where the proposed approach is able to efficiently solve several well-known constraint satisfaction problems. © 2016 Elsevier B.V.