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Valencia, Spain

Merelo J.J.,University of Granada | Mora A.M.,University of Granada | Fernandes C.M.,University of Granada | Esparcia-Alcazar A.I.,S2 Grupo
Natural Computing | Year: 2013

This paper focuses on the iterative design of SofEA, an architecture for distributing evolutionary algorithms (EAs) across computer networks in an asynchronous and decentralized way. SofEA is based on a pool architecture implemented on an object store, allowing the asynchronous interaction with which several clients. The fact that each client is autonomous leads to complex behavior, which will be examined in the work, so that the design can be validated, rules of thumb can be extracted, and the limits of scalability can be found. In this paper we advance the design of an asynchronous, fault-tolerant, and hopefully scalable distributed EA based on the object store CouchDB. We do so by iteratively analyzing running time and average evaluations to solutions on increasingly better versions of the algorithm, looking for the best results, at least from the point of view of running time. By doing so, we increase speed almost fourfold, and also decrement the average number of evaluations to solution in some cases. Experiments have shown also which critical parameters have the bigger influence on the performance in this kind of systems: live population size and number of conflicts, with both being influenced by the number of clients and the size of the population block each client handles at a time. These experiments also show that there is a balance between scalability and fault tolerance, with scalability dropping when a certain number of clients is reached; further clients only increase fault tolerance, at least in the configurations we are using in this paper. The paper also shows that experimentation and measurement conform a good methodology for the design of this kind of asynchronous, heterogeneous and distributed systems, where analytic performance prediction is almost impossible. © 2012 Springer Science+Business Media B.V. Source

Alfaro-Cid E.,Polytechnic University of Valencia | Sharman K.,Polytechnic University of Valencia | Esparcia-Alcazar A.I.,S2 Grupo
Evolutionary Computation | Year: 2014

This work describes an approach devised by the authors for time series classification. In our approach genetic programming is used in combination with a serial processing of data, where the last output is the result of the classification. The use of genetic programming for classification, although still a field where more research in needed, is not new. However, the application of genetic programming to classification tasks is normally done by considering the input data as a feature vector. That is, to the best of our knowledge, there are not examples in the genetic programming literature of approaches where the time series data are processed serially and the last output is considered as the classification result. The serial processing approach presented here fills a gap in the existing literature. This approach was tested in three different problems. Two of them are real world problems whose data were gathered for online or conference competitions. As there are published results of these two problems this gives us the chance to compare the performance of our approach against top performing methods. The serial processing of data in combination with genetic programming obtained competitive results in both competitions, showing its potential for solving time series classification problems. The main advantage of our serial processing approach is that it can easily handle very large datasets. © 2014 by the Massachusetts Institute of Technology. Source

Merelo J.J.,University of Granada | Castillo P.,University of Granada | Mora A.,University of Granada | Esparcia-Alcazar A.I.,S2 Grupo
GECCO 2013 - Proceedings of the 2013 Genetic and Evolutionary Computation Conference | Year: 2013

Solving the MasterMind puzzle, that is, finding out a hidden combination by using hints that tell you how close some strings are to that one is a combinatorial optimization problem that becomes increasingly difficult with string size and the number of symbols used in it. Since it does not have an exact solution, heuristic methods have been traditionally used to solve it; these methods scored each combination using a heuristic function that depends on comparing all possible solutions with each other. In this paper we first optimize the implementation of previous evolutionary methods used for the game of mastermind, obtaining up to a 40% speed improvement over them. Then we study the behavior of an entropy-based score, which has previously been used but not checked exhaustively and compared with previous solutions. The combination of these two strategies obtain solutions to the game of Mastermind that are competitive, and in some cases beat, the best solutions obtained so far. All data and programs have also been published under an open source license. Copyright © 2013 ACM. Source

Merelo J.J.,University of Granada | Mora A.M.,University of Granada | Fernandes C.M.,University of Granada | Esparcia-Alcazar A.I.,S2 Grupo | Laredo J.L.J.,University of Luxembourg
Proceedings - 2012 7th International Conference on P2P, Parallel, Grid, Cloud and Internet Computing, 3PGCIC 2012 | Year: 2012

This paper explores the scalability and performance of pool and island based evolutionary algorithms, both of them using as a mean of interaction an object store, we call this family of algorithms SofEA. This object store allows the different clients to interact asynchronously, the point of the creation of this framework is to build a system for spontaneous and voluntary distributed evolutionary computation. The fact that each client is autonomous leads to a complex behavior that will be examined in the work, so that the design can be validated, rules of thumb can be extracted, and the limits of scalability can be found. In this paper we advance the design of an asynchronous, fault-tolerant and scalable distributed evolutionary algorithm based on the object store CouchDB. We test experimentally the different options and show the trade-offs that pool and island-based solutions offer. © 2012 IEEE. Source

Pagano D.,TU Munich | Juan M.A.,S2 Grupo | Bagnato A.,TXT e solutions | Roehm T.,TU Munich | And 2 more authors.
Proceedings - International Conference on Software Engineering | Year: 2012

Software maintenance and support services are key factors to the customer perception of software product quality. The overall goal of FastFix is to provide developers with a real-time maintenance environment that increases efficiency and reduces costs, improving accuracy in identification of failure causes and facilitating their resolution. To achieve this goal, FastFix observes application execution and user interaction at runtime. We give an overview of the functionality of FastFix and present one of its main application scenarios. © 2012 IEEE. Source

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