Juan Antonio Gómez Pulido

A Differential Evolution Based Algorithm to Optimize the Radio Network Design Problem

In this paper we present a Differential Evolution based algorithm used to solve the Radio Network Design (RND) problem. This problem consists in determining the optimal locations for base station transmitters in order to get a maximum coverage area with a minimum number of transmitters. Because of the very high amount of possible solutions, this problem is suitable to be tackled with evolutionary techniques, so in our work it has been developed an algorithm inspired on the well-known Differential Evolution algorithm, obtaining good results.

AlineaGA--a genetic algorithm with local search optimization for multiple sequence alignment

The alignment and comparison of DNA, RNA and Protein sequences is one of the most common and important tasks in Bioinformatics. However, due to the size and complexity of the search space involved, the search for the best possible alignment for a set of sequences is not trivial. Genetic Algorithms have a predisposition for optimizing general combinatorial problems and therefore are serious candidates for solving multiple sequence alignment tasks. Local search optimization can be used to refine the solutions explored by Genetic Algorithms. We have designed a Genetic Algorithm which incorporates local search for this purpose: AlineaGA. We have tested AlineaGA with representative sequence sets of the globin family. We also compare the achieved results with the results provided by T-COFFEE.

A Parallel Genetic Programming Tool Based on PVM

This paper presents a software package suited for investigating Parallel Genetic Programming (PGP) utilizing Parallel Virtual Machine (PVM) language as means of communicating distributed populations. We show the usefulness of PVM by means of an example developed with this software tool. The example has been run on several processors in a parallel way.

An educational tool for testing caches on symmetric multiprocessors

Abstract In this article, we present a simulator for cache memory systems on symmetric multiprocessors. This simulator is called SMPCache. It has a full graphic and user-friendly interface, and it operates on PC systems with Windows. The simulator has been conceived as a tool for the teaching of cache memories on multiprocessors systems. This tool is very useful to evaluate and understand different design alternatives: the number of processors, the cache coherence protocols, schemes for bus arbitration, mapping, replacement policies, cache size, memory block size, etc. Our experiences in the last three years have demonstrated to us the benefits of the simulator for teaching purposes.

Parallel Niche Pareto AlineaGA--an evolutionary multiobjective approach on multiple sequence alignment.

Multiple sequence alignment is one of the most recurrent assignments in Bioinformatics. This method allows organizing a set of molecular sequences in order to expose their similarities and their differences. Although exact methods exist for solving this problem, their use is limited by the computing demands which are necessary for exploring such a large and complex search space. Genetic Algorithms are adaptive search methods which perform well in large and complex spaces. Parallel Genetic Algorithms, not only increase the speed up of the search, but also improve its efficiency, presenting results that are better than those provided by the sum of several sequential Genetic Algorithms. Although these methods are often used to optimize a single objective, they can also be used in multidimensional domains, finding all possible tradeoffs among multiple conflicting objectives. Parallel AlineaGA is an Evolutionary Algorithm which uses a Parallel Genetic Algorithm for performing multiple sequence alignment. We now present the Parallel Niche Pareto AlineaGA, a multiobjective version of Parallel AlineaGA. We compare the performance of both versions using eight BAliBASE datasets. We also measure up the quality of the obtained solutions with the ones achieved by T-Coffee and ClustalW2, allowing us to observe that our algorithm reaches for better solutions in the majority of the datasets.

AlineaGA: A Genetic Algorithm for Multiple Sequence Alignment

The alignment and comparison of DNA, RNA and Protein sequences is one of the most common and important tasks in Bioinformatics. However, due to the size and complexity of the search space involved, the search for the best possible alignment for a set of sequences is not trivial. Genetic Algorithms have a predisposition for optimizing general combinatorial problems and therefore are serious candidates for solving multiple sequence alignment tasks. We have designed a Genetic Algorithm for this purpose: AlineaGA. We have tested AlineaGA with representative sequence sets of the hemoglobin family. We also present the achieved results so as the comparisons performed with results provided by T-COFFEE.

Optimizing Multiple Sequence Alignment by Improving Mutation Operators of a Genetic Algorithm

Searching for the best possible alignment for a set of sequences is not an easy task, mainly because of the size and complexity of the search space involved. Genetic algorithms are predisposed for optimizing general combinatorial problems in large and complex search spaces. We have designed a Genetic Algorithm for this purpose, AlineaGA, which introduced new mutation operators with local search optimization. Now we present the contribution that these new operators bring to this field, comparing them with similar versions present in the literature that do not use local search mechanisms. For this purpose, we have tested different configurations of mutation operators in eight BAliBASE alignments, taking conclusions regarding population evolution and quality of the final results. We conclude that the new operators represent an improvement in this area, and that their combined use with mutation operators that do not use optimization strategies, can help the algorithm to reach quality solutions.

Control of Bloat in Genetic Programming by Means of the Island Model

This paper presents a new proposal for reducing bloat in Genetic Programming. This proposal is based in a well-known parallel evolutionary model: the island model. We firstly describe the theoretical motivation for this new approach to the bloat problem, and then we present a set of experiments that gives us evidence of the findings extracted from the theory. The experiments have been performed on a representative problem extracted from the GP field: the even parity 5 problem. We analyse the evolution of bloat employing different settings for the parameters employed. The conclusion is that the Island Model helps to prevent the bloat phenomenon.

Parallel AlineaGA: An island parallel evolutionary algorithm for multiple sequence alignment

Multiple sequence alignment is the base of a growing number of Bioinformatics applications. This does not mean that the accuracy of the existing methods corresponds to biologically faultless alignments. Searching for the optimal alignment for a set of sequences is often hindered by the size and complexity of the search space. Parallel Genetic Algorithms are a class of stochastic algorithms which can increase the speed up of the algorithms. They also enhance the efficiency of the search and the robustness of the solutions by delivering results that are better than those provided by the sum of several sequential Genetic Algorithms. AlineaGA is an evolutionary method for solving protein multiple sequence alignment. It uses a Genetic Algorithm on which some of its genetic operators embed a simple local search optimization. We have implemented its parallel version which we now present. Comparing with its sequential version we have observed an improvement in the search for the best solution. We have also compared its performance with ClustalW2 and T-Coffee, observing that Parallel AlineaGA can lead the search for better solutions for the majority of the datasets in study.

Swarm optimisation algorithms applied to large balanced communication networks

In the last years, several combinatorial optimisation problems have arisen in the computer communications networking field. In many cases, for solving these problems it is necessary the use of meta-heuristics. An important problem in communication networks is the Terminal Assignment Problem (TAP). Our goal is to minimise the link cost of large balanced communication networks. TAP is a NP-Hard problem. The intractability of this problem is the motivation for the pursuits of Swarm Intelligence (SI) algorithms that produce approximate, rather than exact, solutions. This paper makes a comparison among the effectiveness of three SI algorithms: Ant Colony Optimisation, Discrete Particle Swarm Optimisation and Artificial Bee Colony. We also compare the SI algorithms with several algorithms from literature. Simulation results verify the effectiveness of the proposed algorithms. The results show that SI algorithms provide good solutions in a better running time.