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To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.cor.2008.08.012 Byline: Sancho Salcedo-Sanz, Emilio G. Ortiz-Garcia, Angel M. Perez-Bellido, Antonio Portilla-Figueras, Francisco Lopez-Ferreras Abstract: This paper proposes a linear programming (LP)-guided Hopfield-genetic algorithm for a class of combinatorial optimization problems which admit a 0-1 integer linear programming. The algorithm modifies the updating order of the binary Hopfield network in order to obtain better performance of the complete hybrid approach. We theoretically analyze several different updating orders proposed. We also include in the paper a novel proposal to guide the Hopfield network using the crossover and mutation operators of the genetic algorithm. Experimental evidences that show the good performance of the proposed approach in two different combinatorial optimization problems are also included in the paper. Author Affiliation: Department of Signal Theory and Communications, Universidad de Alcala de Henares, Campus Universitario, 28871 Alcala de Henares, Madrid, Spain Article Note: (footnote) [star] This work has been partially supported by Universidad de Alcala and Comunidad de Madrid with a Grant no. CCG07-UAH/TIC-1894, and by Ministerio de Educacion with a Grant no. TEC 2006-07010/TCM. A.M. Perez-Bellido and E.G. Ortiz-Garcia are supported by Universidad de Alcala, under the University F.P.I. grants program.

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.cor.2005.05.001 Byline: Sancho Salcedo-Sanz (a), Jing Su (b) Abstract: In this paper we present two strategies for reducing the time of convergence of two metaheuristics (genetic algorithms and simulated annealing) in inductive query by example (IQBE), which is a process for assisting the users of a given information retrieval system in the formulation of queries. Both strategies are based on a reduction of the search space size of the metaheuristics. The first strategy that we introduce is a compression-expansion strategy, where the terms are arranged into sets of a given size. The second strategy we consider is the so-called restricted search, where the number of terms in the genetic algorithm and simulated annealing encodings is fixed to be a given number m. We describe the implementation of the strategies and analyze when they can be successful, and the main drawbacks associated with them. Author Affiliation: (a) Sancho Salcedo-Sanz, Departamento de Teoria de la SeA[+ or -]al y Comunicaciones,Universidad de Alcala, Campus Universitario Carretera Madrid-Barcelona, km. 33, 28871 Alcala de Henares, Madrid, Spain (b) School of Computer Science, The University of Birmingham, B15 2TT, Edgbaston, Birmingham, UK

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.cor.2004.08.010 Byline: Sancho Salcedo-Sanz (a), Yong Xu (b), Xin Yao (b) Abstract: In this paper we tackle the task assignment problem (TSAP) in heterogeneous computer systems. The TSAP consists of assigning a given distributed computer program formed by a number of tasks to a number of processors, subject to a set of constraints, and in such a way a given cost function to be minimized. We introduce a novel formulation of the problem, in which each processor is limited in the number of task it can handle, due to the so called resource constraint. We propose two hybrid meta-heuristic approaches for solving this problem. Both hybrid approaches use a Hopfield neural network to solve the problem's constraints, mixed with a genetic algorithm (GA) and a simulated annealing for improving the quality of the solutions found. We test the performance of the proposed algorithms in several computational TSAP instances, using a GA with a penalty function and a GA with a repairing heuristic for comparison purposes. We will show that both meta-heuristics approaches are very good approaches for solving the TSAP. Author Affiliation: (a) Department of Signal Theory and Communications, Universidad Carlos III de, 28911 Leganes, Madrid, Spain (b) Centre for Research in Computational Intelligence and Applications (CERCIA), School of Computer Science, The University of Birmingham