Gabriel Luque

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The MALLBA project tackles the resolution of combinatorial optimization problems using generic algorithmic skeletons implemented in C++. A skeleton in the MALLBA library implements an optimization method in one of the three families of generic optimization techniques offered: exact, heuristic and hybrid. Moreover, for each of those methods, MALLBA provides(More)
In this paper we propose and study the behavior of a new heuristic algorithm for the DNA fragment assembly problem: PALS. The DNA fragment assembly is a problem to be solved in the early phases of the genome project and thus is very important since the other steps depend on its accuracy. This is an NP-hard combinatorial optimization problem which is growing(More)
This work analyzes the relative advantages of different metaheuristic approaches to the well-known natural language processing problem of part-of-speech tagging. This consists of assigning to each word of a text its disambiguated part-of-speech according to the context in which the word is used. We have applied a classic genetic algorithm (GA), a CHC(More)
The field of parallel metaheuristics is continuously evolving as a result of new technologies and needs that researchers have been encountering. In the last decade, new models of algorithms, new hardware for parallel execution/communication, and new challenges in solving complex problems have been making advances in a fast manner. We aim to discuss here on(More)
This paper presents a study of different models for the growth curves and takeover time in a distributed EA (dEA). The calculation of the takeover time and the dynamical growth curves is a common analytical approach to measure the selection pressure of an EA. This work is a first step to mathematically unify and describe the roles of the migration rate and(More)
When evaluating algorithms a very important goal is to perform better than the state-of-the-art techniques.. This requires experimental tests to compare the new algorithm with respect to the rest. It is, in general, hard to make fair comparisons between algorithms such as metaheuristics. The reason is that we can infer di erent conclusions from the same(More)
In this paper we propose a genetic algorithm (GA) for solving the DNA fragment assembly problem in a computational grid. The algorithm, which is named GrEA, is a steady-state GA which uses a panmitic population, and it is based on computing parallel function evaluations in an asynchronous way. We have implemented GrEA on top of the Condor system, and we(More)