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Initial approaches to the application of islands-based parallel EDAs in continuous domains

JOURNAL OF parallel AND DISTRIBUTED COMPUTING 2006年第8期66卷 991-1001页

作者： delaOssa, Luis Gamez, Jose A. Puerta, Jose A. Univ Castilla La Mancha Dept Sistemas Informat Albacete 02071 Spain

Estimation of distribution algorithms (EDAs) are a wide-ranging family of evolutionary algorithms whose common feature is the way they evolve by learning a probability distribution from the best individuals in a population and sampling it to generate the next one. Although they have been widely applied to solve combinatorial optimization problems, there are also extensions that work with continuous variables. In this paper [this paper is an extended version of delaOssa et al. Initial approaches to the application of islands-based parellel EDAs in continuous domains, in: Proceedings of the 34th International Conference on parallel Processing Workshops (ICPP 2005 Workshops), Oslo, 2005, pp. 580-587] we focus on the solution of the latter by means of island models. Besides evaluating the performance of traditional island models when applied to EDAs, our main goal consists in achieving some insight about the behavior and benefits of the migration of probability models that this framework allow. (C) 2006 Elsevier Inc. All rights reserved.

关键词： parallel evolutionary algorithms estimation of distribution algorithms numerical optimization

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Particle detection optimization by means of a distributed evolutionary algorithm

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MEASUREMENT SCIENCE AND TECHNOLOGY 2007年第8期18卷 2472-2476页

作者： Padee, Adam Zaremba, Krzysztof Kurek, Krzysztof Warsaw Univ Technol Inst Radioelect PL-00665 Warsaw Poland Soltan Inst Nucl Studies PL-00681 Warsaw Poland

This paper presents an approach to optimize the reconstruction of particle tracks in a large area spectrometer using an evolutionary algorithm. First, the basic concepts of the measurement of the momentum are presented, including some details on the track reconstruction. An objective function is formulated to measure the efficiency of the reconstruction. Then, the optimizer is described in two versions: sequential for a single CPU and distributed for a cluster of PCs. The architecture of both of them is presented in detail, including the description of the operators used plus some words on the infrastructure required for the distributed version. Finally, the results from the tests of the program are presented, from which the conclusion is drawn that the distributed genetic algorithm, easy to implement on high energy physics data processing clusters, may be helpful in improving the efficiency of the reconstruction.

关键词： charged particle momentum detection parallel evolutionary algorithms

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General Subpopulation Framework and Taming the Conflict Inside Populations

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evolutionary COMPUTATION 2015年第1期23卷 1-36页

作者： Vargas, Danilo Vasconcellos Murata, Junichi Takano, Hirotaka Botazzo Delbem, Alexandre Claudio Kyushu Univ Grad Sch Informat Sci & Elect Engn Fukuoka 8190395 Japan Kyushu Univ Fac Informat Sci & Elect Engn Fukuoka 8190395 Japan Univ Sao Paulo Inst Math & Comp Sci BR-13566590 Sao Carlos SP Brazil

Structured evolutionary algorithms have been investigated for some time. However, they have been under explored especially in the field of multi-objective optimization. Despite good results, the use of complex dynamics and structures keep the understanding and adoption rate of structured evolutionary algorithms low. Here, we propose a general subpopulation framework that has the capability of integrating optimization algorithms without restrictions as well as aiding the design of structured algorithms. The proposed framework is capable of generalizing most of the structured evolutionary algorithms, such as cellular algorithms, island models, spatial predator-prey, and restricted mating based algorithms. Moreover, we propose two algorithms based on the general subpopulation framework, demonstrating that with the simple addition of a number of single-objective differential evolution algorithms for each objective, the results improve greatly, even when the combined algorithms behave poorly when evaluated alone at the tests. Most importantly, the comparison between the subpopulation algorithms and their related panmictic algorithms suggests that the competition between different strategies inside one population can have deleterious consequences for an algorithm and reveals a strong benefit of using the subpopulation framework.

关键词： Structured evolutionary algorithms parallel evolutionary algorithms hybridization multi-objective algorithms novelty search general subpopulation framework general differential evolution

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Asymptotic guarantee of success for multi-agent memetic systems

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BULLETIN OF THE POLISH ACADEMY OF SCIENCES-TECHNICAL SCIENCES 2013年第1期61卷 257-278页

作者： Byrski, A. Schaefer, R. Smolka, M. Cotta, C. AGH Univ Sci & Technol Dept Comp Sci 30 Mickiewicza Av PL-30059 Krakow Poland Univ Malaga ETSI Informat Dept Lenguajes & Ciencias Computac E-29071 Malaga Spain

The paper introduces a stochastic model for a class of population-based global optimization meta-heuristics, that generalizes existing models in the following ways. First of all, an individual becomes an active software agent characterized by the constant genotype and the meme that may change during the optimization process. Second, the model embraces the asynchronous processing of agent's actions. Third, we consider a vast variety of possible actions that include the conventional mixing operations (e.g. mutation, cloning, crossover) as well as migrations among demes and local optimization methods. Despite the fact that the model fits many popular algorithms and strategies (e.g. genetic algorithms with tournament selection) it is mainly devoted to study memetic algorithms. The model is composed of two parts: EMAS architecture (data structures and management strategies) allowing to define the space of states and the framework for stochastic agent actions and the stationary Markov chain described in terms of this architecture. The probability transition function has been obtained and the Markov kernels for sample actions have been computed. The obtained theoretical results are helpful for studying metaheuristics conforming to the EMAS architecture. The designed synchronization allows the safe, coarse-grained parallel implementation and its effective, sub-optimal scheduling in a distributed computer environment. The proved strong ergodicity of the finite state Markov chain results in the asymptotic stochastic guarantee of success, which in turn imposes the liveness of a studied metaheuristic. The Markov chain delivers the sampling measure at an arbitrary step of computations, which allows further asymptotic studies, e.g. on various kinds of the stochastic convergence.

关键词： computational multi-agent systems asymptotic analysis global optimization parallel evolutionary algorithms Markov chain modeling

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On the choice of the offspring population size in evolutionary algorithms

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evolutionary COMPUTATION 2005年第4期13卷 413-440页

作者： Jansen, T De Jong, KA Wegener, I Univ Dortmund FB Informat D-44221 Dortmund Germany George Mason Univ Krasnow Inst Fairfax VA 22030 USA

evolutionary algorithms (EAs) generally come with a large number of parameters that have to be set before the algorithm can be used. Finding appropriate settings is a difficult task. The influence of these parameters on the efficiency of the search performed by an evolutionary algorithm can be very high. But there is still a lack of theoretically justified guidelines to help the practitioner find good values for these parameters. One such parameter is the offspring population size. Using a simplified but still realistic evolutionary algorithm, a thorough analysis of the effects of the offspring population size is presented. The result is a much better understanding of the role of offspring population size in an EA and suggests a simple way to dynamically adapt this parameter when necessary.

关键词： evolutionary algorithms evolution strategies plus-selection offspring population size optimization time run time parallel evolutionary algorithms

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A Statistical Study of a Class of Cellular evolutionary algorithms

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evolutionary COMPUTATION 1999年第3期7卷 255-274页

作者： Capcarrere, Mathieu Tomassini, Marco Tettamanzi, Andrea Sipper, Moshe Swiss Fed Inst Technol Log Syst Lab CH-1015 Lausanne Switzerland Univ Lausanne Inst Comp Sci CH-1015 Lausanne Switzerland Univ Milan Dept Comp Sci I-26013 Crema CR Italy

parallel evolutionary algorithms, over the past few years, have proven empirically worthwhile, but there seems to be a lack of understanding of their workings. In this paper we concentrate on cellular (fine-grained) models, our objectives being: (1) to introduce a suite of statistical measures, both at the genotypic and phenotypic levels, which are useful for analyzing the workings of cellular evolutionary algorithms;and (2) to demonstrate the application and utility of these measures on a specific example-the cellular programming evolutionary algorithm. The latter is used to evolve solutions to three distinct (hard) problems in the cellular-automata domain: density, synchronization, and random number generation. Applying our statistical measures, we are able to identify a number of trends common to all three problems (which may represent intrinsic properties of the algorithm itself), as well as a host of problem-specific features. We find that the evolutionary algorithm tends to undergo a number of phases which we are able to quantitatively delimit. The results obtained lead us to believe that the measures presented herein may prove useful in the general case of analyzing fine-grained evolutionary algorithms.

关键词： parallel evolutionary algorithms cellular automata cellular programming genetic algorithms statistical analysis

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An improved CUDA-based hybrid metaheuristic for fast controller of an evolutionary robot

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INTERNATIONAL JOURNAL OF EMBEDDED SYSTEMS 2018年第5期10卷 406-422页

作者： Benalia, Nour El-Houda Djedi, NourEddine Bitam, Salim Ouannes, Nesrine Duthen, Yves Univ Biskra Dept Comp Sci LESIA Lab Biskra Algeria IRIT Lab Vortex Team Toulouse France

This paper proposes a novel parallel hybrid training approach to conceive an evolutionary robot. The proposed design aims to provide efficient behaviours to perform its tasks in a complex area such as walking toward a hidden destination. Embedded in robot brain, this training and evolution combination is typically accomplished by evolving considerable recurrent neural networks (RNNs) using an evolutionary strategy (ES). The effectiveness of this proposal is improved by employing CUDA technology that executes the evolutionary process of RNNs in a parallel way. The modifications applied are indicating to meet CUDA requirements in terms of CPU/GPU cooperation and memory management. Using a set of experiments performed by GPGPU-based physical simulator named open dynamics engine (ODE) and CUDA-based evolution, the effectiveness of the proposed parallel evolutionary training technique was validated for real movements of humanoid robots. This validation showed a promising speed-up, since this field requires very high powerful computational resources.

关键词： artificial life robotics parallel evolutionary algorithms recurrent neural network RNN GPU

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evolutionary algorithms for aerofoil design

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INTERNATIONAL JOURNAL OF COMPUTATIONAL FLUID DYNAMICS 1998年第1-2期11卷 51-77页

作者： De Falco, I Della Cioppa, A Iazzetta, A Tarantino, E Natl Res Council Italy CNR IRSIP I-80131 Naples Italy

This paper wishes to describe evolutionary algorithms as an effective means for the solution of the Aerofoil Design Optimisation in Aerodynamics. Firstly the basic ideas underlying evolutionary algorithms are outlined. Several versions of evolutionary algorithms are briefly described, focussing on their similarities and on their differences as well. Then their application to both Direct and Inverse Aerofoil Design Problem is described, and results are given. Finally, several possible parallel models for evolutionary algorithms are discussed, and the results of the application of one of them to the above problem are presented.

关键词： optimisation aerofoil design evolutionary algorithms genetic algorithms evolution strategies breeder genetic algorithms evolutionary programming parallel evolutionary algorithms

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Why Asynchronous parallel Evolution is the Future of Hyper-heuristics: A CDCL SAT Solver Case Study

Why Asynchronous Parallel Evolution is the Future of Hyper-h...

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Genetic and evolutionary Computation Conference (GECCO)

作者： Bertels, Alex R. Tauritz, Daniel R. Missouri Univ Sci & Technol Dept Comp Sci Nat Computat Lab Rolla MO 65409 USA

ISBN: (纸本)9781450343237

evolutionary algorithms (EAs) are inherently parallel due to their ability to simultaneously evaluate the fitness of individuals. Synchronous parallel EAs (SPEAs) leverage this with the intent to gain significant speed-ups when executed on multiple processors. However, many important problem classes lead to large variations in fitness evaluation times, such as is often the case in hyper-heuristics where the time complexity of executing one individual may differ greatly from that of another. Asynchronous parallel EAs (APEAs) omit the generational synchronization step of traditional EAs which work in well-defined cycles. They can provide scalability improvements proportional to the variation in fitness evaluation times of the evolved individuals, and therefore should be considered for use in hyper-heuristics. This paper provides an empirical analysis of the improvements obtained by applying APEAs, compared to SPEAs, on a case study involving the evolution of conflict-driven clause learning Boolean satisfiability solvers, demonstrating that APEAs are the future of hyper-heuristics.

关键词： Hyper-Heuristics Asynchronous parallel Evolution parallel evolutionary algorithms Genetic Programming SAT

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parallel Cooperation for Large-Scale Multiobjective Optimization on Feature Selection Problems 18

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18th European Conference on Applications of evolutionary Computation (EvoApplications) as part of EvoSTAR Conference

作者： Kimovski, Dragi Ortega, Julio Ortiz, Andres Banos, Raul Univ Informat Sci & Technol Ohrid North Macedonia Univ Granada Dept Comp Architecture & Technol CITIC Granada Spain Univ Malaga Dept Commun Engn Malaga Spain Catholic Univ Murcia Dept Business Adm & Management Murcia Spain

ISBN: (纸本)9783319165486;9783319165493

Recently, the interest on multiobjective optimization problems with a large number of decision variables has grown since many significant real problems, for example on machine learning and pattern recognition, imply to process patterns with a high number of components (features). This paper deals with parallel multiobjective optimization on high-dimensional feature selection problems. Thus, several parallel multiobjective evolutionary alternatives based on the cooperation of subpopulations are proposed and experimentally evaluated by using some synthetic and BCI (Brain-Computer Interface) benchmarks. The results obtained show different improvements achieved in the solution quality and speedups, depending on the parallel alternative and benchmark profile. Some alternatives even provide superlinear speedups with only small reductions in the solution quality.

关键词： Cooperative coevolution Feature selection Multiobjective clustering parallel evolutionary algorithms

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