Gabriel Luque

Parallel metaheuristics: recent advances and new trends

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 the state of the art, in a summarized manner, to provide a solution to deal with some of the growing topics. These topics include the utilization of classic parallel models in recent platforms (such as grid/cloud architectures and GPU/APU). However, porting existing algorithms to new hardware is not enough as a scientific goal, therefore researchers are looking for new parallel optimization and learning models that are targeted to these new architectures. Also, parallel metaheuristics, such as dynamic optimization and multiobjective problem resolution, have been applied to solve new problem domains in past years. In this article, we review these recent research areas in connection to parallel metaheuristics, as well as we identify future trends and possible open research lines for groups and PhD students.

MALLBA: a software library to design efficient optimisation algorithms

In this paper we discuss on the MALLBA framework, a software tool for the resolution of combinatorial optimisation problems using generic algorithmic skeletons implemented in C++. Every skeleton in the MALLBA library implements an optimisation method (exacts, metaheuristics and hybrids) and provides three different implementations for it: sequential, parallel for Local Area Networks and parallel for Wide Area Networks. This paper introduces some aspects about the software design of the MALLBA library, details of the most recent implemented skeletons and offers computational results for a scheduling problem to illustrate the utilisation of our library.

Efficient parallel LAN/WAN algorithms for optimization: the MALLBA project

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 three different implementations: sequential, parallel for Local Area Networks, and parallel for Wide Area Networks. This paper introduces the architecture of the MALLBA library, details some of the implemented skeletons, and offers computational results for some classical optimization problems to show the viability of our library. Among other conclusions, we claim that the design used to develop the optimization techniques included in the library is generic and efficient at the same time.

Natural language tagging with genetic algorithms

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 algorithm, and a simulated annealing (SA). Different ways of encoding the solutions to the problem (integer and binary) have been studied, as well as the impact of using parallelism for each of the considered methods. We have performed experiments on different linguistic corpora and compared the results obtained against other popular approaches plus a classic dynamic programming algorithm. Our results claim for the high performances achieved by the parallel algorithms compared to the sequential ones, and state the singular advantages for every technique. Our algorithms and some of its components can be used to represent a new set of state-of-the-art procedures for complex tagging scenarios.

DNA fragment assembly using a grid-based genetic algorithm

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 have used it to solve the DNA assembly problem. This is an NP-hard combinatorial optimization problem which is growing in importance and complexity as more research centers become involved on sequencing new genomes. While previous works on this problem have usually faced 30K base pairs (bps) long instances, we have tackled here a 77K bps long one to show how a grid system can move research forward. After analyzing the basic grid algorithm, we have studied the use of an improvement method to still enhance its scalability. Then, by using a grid composed of up to 150 computers, we have achieved time reductions from tens of days down to a few hours, and we have obtained near optimal solutions when solving the 77K bps long instance (773 fragments). We conclude that our proposal is a promising approach to take advantage of a grid system to solve large DNA fragment assembly problem instances and also to learn more about grid metaheuristics as a new class of algorithms for really challenging problems.

Autocorrelation measures for the quadratic assignment problem

Abstract In this article we provide an exact expression for computing the autocorrelation coefficient ξ and the autocorrelation length l of any arbitrary instance of the Quadratic Assignment Problem (QAP) in polynomial time using its elementary landscape decomposition. We also provide empirical evidence of the autocorrelation length conjecture in QAP and compute the parameters ξ and l for the 137 instances of the QAPLIB. Our goal is to better characterize the difficulty of this important class of problems to ease the future definition of new optimization methods. Also, the advance that this represents helps to consolidate QAP as an interesting and now better understood problem.

A new local search algorithm for the DNA fragment assembly problem

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 in importance and complexity as more research centers become involved on sequencing new genomes. Various heuristics, including genetic algorithms, have been designed for solving the fragment assembly problem, but since this problem is a crucial part of any sequencing project, better assemblers are needed. Our proposal is a very efficient assembler that allows to find optimal solutions for large instances of this problem, considerably faster than its competitors and with high accuracy.

Seeding strategies and recombination operators for solving the DNA fragment assembly problem

The fragment assembly problem consists in building the DNA sequence from several hundreds (or even, thousands) of fragments obtained by biologists in the laboratory. This is an important task in any genome project since the rest of the phases depend on the accuracy of the results of this stage. Therefore, accurate and efficient methods for handling this problem are needed. Genetic Algorithms (GAs) have been proposed to solve this problem in the past but a detailed analysis of their components is needed if we aim to create a GA capable of working in industrial applications. In this paper, we take a first step in this direction, and focus on two components of the GA: the initialization of the population and the recombination operator. We propose several alternatives for each one and analyze the behavior of the different variants. Results indicate that using a heuristically generated initial population and the Edge Recombination (ER) operator is the best approach for constructing accurate and efficient GAs to solve this problem.

Theoretical models of selection pressure for dEAs: topology influence

This paper presents a study of different models for the best individuals growth curve and the takeover time in a distributed evolutionary algorithm (dEA). The calculation of the takeover time is a common analytical approach to measure the selection pressure of an EA. This work is another step forward to mathematically unify and describe the roles of several parameters of the migration policy: the migration rate, the migration frequency, and the topology in the selection pressure induced by the dynamics of dEAs. In order to achieve these goals we comparatively evaluate the appropriateness of the well-known panmictic logistic model, hypergraph model and two new models for dEAs. We introduce new accurate models for growth curves and takeover times in dEAs, and analytically explain the effects of the migration rate, migration frequency, and topology

A Hybrid Genetic Algorithm for the DNA Fragment Assembly Problem

In this paper we propose and study the behavior of a new hybrid heuristic algorithm for the DNA fragment assembly problem. The DNA fragment assembly is a problem solved in the early phases of the genome project and thus very important, since the other steps depend on its accuracy. This is an NP-hard combinatorial optimization problem which is growing in importance and complexity as more research centers become involved on sequencing new genomes. Our contribution is a hybrid method that combines a promising heuristic, PALS, with a well-know metaheuristic, a genetic algorithm, obtaining as result a very efficient assembler that allows to find optimal solutions for large instances of this problem.