Miguel A. Vega Rodríguez

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.

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.

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.

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.

Placement and routing of Boolean functions in constrained FPGAs using a distributed genetic algorithm and local search

In this work we present a system for implementing the placement and routing stages in the FPGA cycle of design, into the physical design stage. We start with the ISCAS benchmarks, on EDIF format, of Boolean functions to be implemented. They are processed by a parser in order to obtain an internal representation which is able to be processed by a genetic algorithm (GA) tool. This tool develops the placement and routing tasks, considering possible restricted area into the FPGA. In order to help to the GA to make the routing stage we have added a local search procedure. That local search gets a path between two points without considering neither their placement nor the restricted areas among them. The GA is fully customizable, featuring the ability to work with one or several islands. The experiments have verified that using distributing execution improves the costs and speeds up the convergence towards better results in smaller slots of time

Reconfigurable Computing and Parallelism for Implementing and Accelerating Evolutionary Algorithms

Reconfigurable Computing is a technique for executing algorithms directly on the hardware in order to accelerate and increase their performance. Reconfigurable hardware consists of programmed FPGA chips for working as specific purpose coprocessors. The algorithms to be executed are programmed by means of description hardware languages and implemented in hardware using synthesis tools. Reconfigurable Computing is very useful for processing high computational cost algorithms because the algorithms implemented in a specific hardware get greater performance than if they are processed by a general purpose conventional processor. So Reconfigurable Computing and parallel techniques have been applied on a genetic algorithm for solving the salesman problem and on a parallel evolutionary algorithm for time series predictions. The hardware implementation of these two problems allows a wide set of tools and techniques to be shown. In both cases satisfactory experimental performances have been obtained.

DNS weighted footprints for web browsing analytics

Abstract The monetization of the large amount of data that ISPs have of their users is still in early stages. Specifically, the knowledge of the websites that specific users or aggregates of users visit opens new opportunities of business, after the convenient sanitization. However, the construction of accurate DNS-based web-user profiles on large networks is a challenge not only because the requirements that capturing traffic entails, but also given the use of DNS caches, the proliferation of botnets and the complexity of current websites (i.e., when a user visit a website a set of self-triggered DNS queries for banners, from both same company and third parties services, as well for some preloaded and prefetching contents are in place). In this way, we propose to count the intentional visits users make to websites by means of DNS weighted footprints. Such novel approach consists of considering that a website was actively visited if an empirical-estimated fraction of the DNS queries of both the own website and the set of self-triggered websites are found. This approach has been coded in a final system named DNS prints . After its parameterization (i.e., balancing the importance of a website in a footprint with respect to the total set of footprints), we have measured that our proposal is able to identify visits and their durations with false and true positives rates between 2 and 9% and over 90%, respectively, at throughputs between 800,000 and 1.4 million DNS packets per second in diverse scenarios, thus proving both its refinement and applicability.