Examinando por Autor "Galleguillos, C."
Mostrando 1 - 7 de 7
Resultados por página
Opciones de ordenación
Ítem Acceso Abierto A filtering technique for helping to solve sudoku problems(Springer Verlag, 2015) Soto, R.; Crawford, B.; Galleguillos, C.; Crawford, K.; Paredes, F.This paper highlights the current usability issues when solving Sudoku problems. This problem is a well-known puzzle game which consists in assigning numbers in a game board, commonly of 9 × 9 size. The board of the game is composed of 9 columns, 9 rows and 9 3 × 3 sub-grids; each one containing 9 cells with distinct integers from 1 to 9. A game is completed when all cells have a value assigned, and the previous constraints are satisfied. Some instances are very difficult to solve, to tackle this issue, we have used a filtering technique named Arc Consistency 3 (AC3) from the Constraint Programming domain. This algorithm has revealed which is much related to the strategies employed by users in order to solve the Sudoku instances, but in contrast, this technique is executed in a short time, offering a good resolution guide to the users. In general, filtering techniques make easier solving Sudoku puzzles, providing good information to users for this. © Springer International Publishing Switzerland 2015.Ítem Acceso Abierto Automated, adaptive, and optimized search for CSPs via cuckoo search(Springer Verlag, 2015) Soto, R.; Crawford, B.; Flores J.; Mella F.; Galleguillos, C.; Johnson, F.; Paredes, F.Constraint Programing is a programming paradigm devoted to the efficient solving of constraint satisfaction problems (CSPs). A CSP is a formal problem representation mainly composed of variables and constraints defining relations among those variables. The resolution process of CSPs is commonly carried out by building and exploring a search tree that holds the possibles solutions. Such a tree is dynamically created by interleaving two different phases: enumeration and propagation. During enumeration, the variables and values are chosen to build the possible solution, while propagation intend to delete the values having no chance to reach a feasible result. Autonomous Search is a new technique that gives the ability to the resolution process to be adaptive by re-configuring its enumeration strategy when poor performances are detected. This technique has exhibited impressive results during the last years. However, such a re-configuration is hard to achieve as parameters are problem-dependent and their best configuration is not stable along the search. In this paper, we introduce an Autonomous Search framework that incorporates a new optimizer based on Cuckoo Search able to efficiently support the re-configuration phase. Our goal is to provide an automated, adaptive, and optimized search system for CSPs. We report encouraging results where our approach clearly improves the performance of previously reported Autonomous Search approaches for CSPs. © Springer International Publishing Switzerland 2015.Ítem Acceso Abierto Autonomous tuning for constraint programming via artificial bee colony optimization(Springer Verlag, 2015) Soto, R.; Crawford, B.; Mella F.; Flores J.; Galleguillos, C.; Misra, S.; Johnson, F.; Paredes, F.Constraint Programming allows the resolution of complex problems, mainly combinatorial ones. These problems are defined by a set of variables that are subject to a domain of possible values and a set of constraints. 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. Autonomous Search provides the ability to the solver to self-tune its enumeration strategy in order to select the most appropriate one for each part of the search tree. This self-tuning process is commonly supported by an optimizer which attempts to maximize the quality of the search process, that is, to accelerate the resolution. In this work, we present a new optimizer for self-tuning in constraint programming based on artificial bee colonies. We report encouraging results where our autonomous tuning approach clearly improves the performance of the resolution process. © Springer International Publishing Switzerland 2015.Ítem Acceso Abierto Comparing cuckoo search, bee colony, firefly optimization, and electromagnetism-like algorithms for solving the set covering problem(Springer Verlag, 2015) Soto, R.; Crawford, B.; Galleguillos, C.; Barraza J.; Lizama S.; Muñoz A.; Vilches J.; Misra, S.; Paredes, F.The set covering problem is a classical model in the subject of combinatorial optimization for service allocation, that consists in finding a set of solutions for covering a range of needs at the lowest possible cost. In this paper, we report various approximate methods to solve this problem, such as Cuckoo Search, Bee Colony, Firefly Optimization, and Electromagnetism-Like Algorithms. We illustrate experimental results of these metaheuristics for solving a set of 65 non-unicost set covering problems from the Beasley’s OR-Library. © Springer International Publishing Switzerland 2015.Ítem Acceso Abierto Resolución de nonogramas usando algoritmos genéticos [Solving nonogram using genetic algorithms](IEEE Computer Society, 2016) Soto, R.; Crawford, B.; Galleguillos, C.; Olguin, E.A nonogram corresponds to a puzzle game. The aim in this game is generating an image on a grid by checking certain cells, this cells have to satisfy some rules associated with each row and column. In this paper, genetic algorithms are used to solve this problem, applying certain improvements that will benefit the process of seeking solutions. © 2016 AISTI.Ítem Acceso Abierto The complexity of designing and implementing metaheuristics(Springer Verlag, 2015) Soto, R.; Crawford, B.; Olivares, R.; Galleguillos, C.; Crawford, K.; Johnson, F.; Paredes, F.Optimization problems can be found in several real application domains such as engineering, medicine, mathematics, mechanics, physics, mining, games, design, and biology, among others. There exist several techniques to the efficient solving of these problems, which can be organized in two groups: exact and approximate methods. Metaheuristics are one of the most famous and widely used approximate methods for solving optimization problems. Most of them are known for being inspired on interesting behaviors that can be found on the nature, such as the way in which ants, bees and fishes found food, or the way in which fireflies and bats move on the environment. However, solving optimization problems via metaheuristics is not always a simple trip. In this paper, we analyze and discuss from an usability standpoint how the effort needed to design and implement efficient and robust metaheuristics can be conveniently managed and reduced. © Springer International Publishing Switzerland 2015.Ítem Acceso 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.