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Evolving processes and evolution schedulers for concurrent scheduling controls and parallel evolutionary computation

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FUTURE GENERATION COMPUTER SYSTEMS 1998年第5-6期14卷 365-381页

作者： Elrad, T Lin, JL IIT Chicago IL 60616 USA Lucent Technol ADSL Element Management Naperville IL 60566 USA

In this paper we present concepts of evolution scheduling, which will provide us with a better scheduling mechanism for competing processes in the ready-queue, the blocked-queue, the pc (priority control) queue, and the cs (client-and-server) queue. The evolution scheduling is acceptable for the soft real-time system and useful for its performance to achieve fair and effective scheduling. On the other hand, for those who pursue evolutionary computation, this is good news. It allows them to carry out natural competition in an easy way consistent with their operating system and programming language. (C) 1998 Elsevier Science B.V. All rights reserved.

关键词： evolving process evolution scheduling parallel evolutionary computation concurrent scheduling control

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An Analytical Study of GPU computation for Solving QAPs by parallel evolutionary computation with Independent Run

An Analytical Study of GPU Computation for Solving QAPs by P...

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2010 IEEE World Congress on computational Intelligence

作者： Tsutsui, Shigeyoshi Fujimoto, Noriyuki Dept Management & Informat Sci 5-4-33 Amammihigashi Osaka 5808502 Japan Osaka Prefecture Univ Grad Sch Sci Osaka 5998531 Japan

ISBN: (纸本)9781424481262

This paper proposes an evolutionary algorithm for solving QAPs with parallel independent run using GPU computation and give sastatistical analysis on how speedup can be attained with this model. With the proposed model, we achieve a GPU computation performance that is nearly proportional to the number of equipped multi-processors (MPs) in the GPUs. We explain these computational results by performing statistical analysis. Regarding performance comparison to CPU computations, GPU computation shows a speedup of x4.4 and x7.9 on average using a single GPU and two GPUs, respectivey

关键词： GPU computation QAP coprocessors evolutionary algorithm evolutionary computation independent run multiprocessors parallel evolutionary computation parallel processing statistical analysis statistical analysis

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Migration Effects on Tree Topology of parallel evolutionary computation

Migration Effects on Tree Topology of Parallel Evolutionary ...

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IEEE Region 10 Conference on TENCON 2010

作者： Miyagi, Hayato Tengan, Takeshi Mohamed, Said Nakamura, Morikazu Univ Ryukyus Dept Informat Eng Okinawa 9030213 Japan Meio Univ Fac Int Studies Okinawa 9058585 Japan

ISBN: (纸本)9781424468904

Migration of individuals among islands is effective in many cases in distributed parallel processing of the evolutionary computation. However, the effectiveness of migration depends on many factors such as migration frequency, migration topologies, migration scale, timing, and so on. The objective of our research is to investigate such factors for designing efficient parallel evolutionary computation. In this paper we analyzes the migration effects of the tree based island model in parallel genetic algorithms and parallel PBIL (Population Based Incremental Learning). computational experiment shows us migration effects of the parallel evolutionary computation and influence of the migration topology and frequency.

关键词： distributed parallel processing genetic algorithms learning (artificial intelligence) migration effects parallel PBIL parallel evolutionary computation parallel genetic algorithms parallel processing population based incremental learning topology tree bas

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Optimal Path Planning Generation for Mobile Robots using parallel evolutionary Artificial Potential Field

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JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS 2015年第2期79卷 237-257页

作者： Montiel, Oscar Sepulveda, Roberto Orozco-Rosas, Ulises Inst Politecn Nacl CITEDI Tijuana 22510 Baja California Mexico

In this paper, we introduce the concept of parallel evolutionary Artificial Potential Field (PEAPF) as a new method for path planning in mobile robot navigation. The main contribution of this proposal is that it makes possible controllability in complex real-world sceneries with dynamic obstacles if a reachable configuration set exists. The PEAPF outperforms the evolutionary Artificial Potential Field (EAPF) proposal, which can also obtain optimal solutions but its processing times might be prohibitive in complex real-world situations. Contrary to the original Artificial Potential Field (APF) method, which cannot guarantee controllability in dynamic environments, this innovative proposal integrates the original APF, evolutionary computation and parallel computation for taking advantages of novel processors architectures, to obtain a flexible path planning navigation method that takes all the advantages of using the APF and the EAPF, strongly reducing their disadvantages. We show comparative experiments of the PEAPF against the APF and the EAPF original methods. The results demonstrate that this proposal overcomes both methods of implementation;making the PEAPF suitable to be used in real-time applications.

关键词： Mobile robot navigation Path planning Artificial potential field APF PEAPF parallel evolutionary computation

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A client/server-based parallel coevolutionary algorithm for parallel machines scheduling problem with penalties

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JOURNAL OF THE CHINESE INSTITUTE OF ENGINEERS 2015年第2期38卷 139-148页

作者： Hu, Shicheng He, Qingsong Liu, Yang Harbin Inst Technol Sch Econ & Management Weihai 264209 Peoples R China Harbin Inst Technol Sch Comp Sci & Technol Weihai 264209 Peoples R China

A problem of n independent jobs with different ready times and due dates to be scheduled on m parallel machines which aims at minimizing the total tardiness penalty costs of all jobs is considered. The decomposition and combination characteristics of the problem are studied. Based on these two characteristics, a parallel coevolutionary algorithm (PCA) is proposed. The PCA is implemented in a client/server (C/S)-based parallel coevolutionary computation structure, in which the server is responsible for global evolution by task assignment and the client is responsible for local evolution by task ordering. According to this structure, a genetic algorithm and a virus evolutionary genetic algorithm are developed, which are executed on server and client, respectively. The simulated experiments are designed, the comparison results with adapted algorithms show that the proposed algorithm C/S-PCA has superior performances both in convergence speed and in searching optimality.

关键词： client/server-based computation parallel evolutionary computation parallel machines scheduling virus evolution genetic algorithm

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PEA: parallel evolutionary Algorithm by Separating Convergence and Diversity for Large-Scale Multi-Objective Optimization 38

PEA: Parallel Evolutionary Algorithm by Separating Convergen...

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38th IEEE International Conference on Distributed Computing Systems (ICDCS)

作者： Chen, Huangke Zhu, Xiaomin Pedrycz, Witold Yin, Shu Wu, Guohua Yan, Hui Natl Univ Def Technol Coll Syst Engn Changsha 410073 Peoples R China Univ Alberta Dept Elect & Comp Engn Edmonton AB T6G 2V4 Canada ShanghaiTech Univ SIST Shanghai Peoples R China

ISBN: (纸本)9781538668719

Running evolutionary algorithms in parallel is an intuitive way to speed up the process of solving large-scale multi-objective optimization problems, which have hundreds or thousands of decision variables. However, the framework of the existing multi-objective evolutionary algorithms seriously limits their parallelization. During each iteration, the environmental selection operators present in the existing framework need to collect and compare all the candidate solutions to balance the convergence and diversity, thus dividing the whole evolutionary process into a series of dependent sub-processes and resulting in frequent data transmission. To address this issue, we propose a novel parallel framework that separates the environmental selection operator from the entire evolutionary process, evidently removing the dependencies among sub-processes and reducing the data transmission. On the basis of the parallel framework, a new parallel evolutionary algorithm, namely PEA, is designed. In PEA, the convergence is achieved by a series of independent sub-populations, and the diversity is merely emphasized at the converged solutions from each sub population, which is helpful for avoiding that the environmental selection operator limits the parallelization of the algorithm. Moreover, a new environmental selection strategy is proposed to improve the diversity without considering the convergence. To assess the performance of the proposed PEA, we compare it with five representative multi-objective evolutionary algorithms in terms of both the convergence and diversity. The performance of the parallel framework is also analyzed by comparing with two existing parallel models. The experimental results demonstrate the superiority of the proposed parallel algorithms in terms of the convergence, diversity, and speedup.

关键词： parallel evolutionary computation evolutionary algorithm large-scale multi-objective

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The parallel Single Front Genetic Algorithm (PSFGA) in dynamic multi-objective optimization

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9th International Work-Conference on Artificial Neural Networks

作者： Camara, Mario Ortega, Julio De Toro, Francisco Univ Granada ETSIIT Dept Commun Architecture & Technol Granada Spain Univ Granada ETSIIT Dept Signals Telemat & Commun Granada Spain

ISBN: (纸本)9783540730064

This paper analyzes the use of the, previously proposed, parallel Single Front Genetic Algorithm (PSFGA) in applications in which the objective functions, the restrictions, and hence also solutions can change over the time. These dynamic optimization problems appear in quite different real applications with relevant socio-economic impacts. PSFGA uses a master process that distributes the population among the processors in the system (that evolve their corresponding solutions according to an island model), and collects and adjusts the set of local Pareto fronts found by each processor (this way, the master also allows an implicit communication among islands). The procedure exclusively uses non-dominated individuals for the selection and variation, and maintains the diversity of the approximation to the Pareto front by using a strategy based on a crowding distance.

关键词： dynamic optimization problems parallel evolutionary computation single front multi-objective optimization parallel processing

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Rule Weight Update in parallel Distributed Fuzzy Genetics-Based Machine Learning with Data Rotation

Rule Weight Update in Parallel Distributed Fuzzy Genetics-Ba...

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IEEE International Conference on Fuzzy Systems (FUZZ)

作者： Ishibuchi, Hisao Yamane, Masakazu Nojima, Yusuke Osaka Prefecture Univ Grad Sch Engn Dept Comp Sci & Intelligent Syst Sakai Osaka 5998531 Japan

ISBN: (纸本)9781479900206

In our former study, we have already proposed a parallel distributed model for the speedup of fuzzy genetics-based machine learning (GBML). Our model is an island model for parallel implementation of fuzzy GBML algorithms where a population is divided into multiple subpopulations. A single subpopulation is assigned to each island. Training data are also divided and distributed over the islands. When we have N islands (i.e., N CPUs for parallel computation), the speedup is the order of the square of N. This is because both the population and the training data are divided into N subsets. One characteristic feature of our parallel distributed model is training data rotation over the islands. Each of the N training data subsets is assigned to one of the N islands. The assigned training data subsets are rotated over the islands periodically (e.g., every 100 generations). This means that the environment of each island is changed periodically. The focus of this paper is how to update existing fuzzy rules at each island after the training data rotation. One extreme setting is to totally update fuzzy rules using the newly assigned training data subset. Another extreme setting is to use existing fuzzy rules with no changes. In this paper, we examine incremental learning, which can be viewed as an intermediate mechanism between the two extreme settings.

关键词： Genetics-based machine learning (GBML) genetic fuzzy systems (GFS) parallel evolutionary computation Pittsburgh approach fuzzy rule-based classifiers pattern classification

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A parallel Multi-objective Ab initio Approach for Protein Structure Prediction

A Parallel Multi-objective <i>Ab initio</i> Approach for Pro...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Becerra, David Sandoval, Angelica Restrepo-Montoya, Daniel Nino, Luis F. Univ Nacl Colombia Intelligent Syst Res Lab LISI Bogota Colombia Univ Nacl Colombia Algorithms & Combinator Res Grp ALGOS Bogota Colombia

ISBN: (纸本)9781424483075

Protein structure prediction is one of the most important problems in bioinformatics and structural biology. This work proposes a novel and suitable methodology to model protein structure prediction with atomic-level detail by using a parallel multi-objective ab initio approach. In the proposed model, i) A trigonometric representation is used to compute backbone and side-chain torsion angles of protein atoms;ii) The Chemistry at HARvard Macromolecular Mechanics (CHARMm) function optimizes and evaluates the structures of the protein conformations;iii) The evolution of protein conformations is directed by optimization of protein energy contributions using the multi-objective genetic algorithm NSGA-II;and iv) The computation process is sped up and its effectiveness improved through the implementation of an island model of the evolutionary algorithm. The proposed model was validated on a set of benchmark proteins obtaining very promising results.

关键词： Bioinformatics Protein Structure Prediction Ab-initio methods parallel evolutionary computation Multi-objective optimization

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A parallel Framework for Multi-objective evolutionary Optimization

A Parallel Framework for Multi-objective Evolutionary Optimi...

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2010 IEEE World Congress on computational Intelligence

作者： Dasgupta, Dipankar Becerra, David Banceanu, Alex Nino, Fernando Simien, James Univ Memphis ISSRL Memphis TN 38152 USA Natl Univ Columbia Intelligent Syst Res Lab Columbia SC USA Navy Personnel Res Studies & Technol Millington TN USA

ISBN: (纸本)9781424481262

This work focuses on the development of a parallel framework method to improve the effectiveness and the efficiency of the obtained solutions by Multi-objective evolutionary Algorithms. Specifically, a parallel architecture based on Java-Spaces technology and an island paradigm model is proposed and tested on two important and complex computational problems: The Protein Structure Prediction and the Task based Navy's Sailor Assignment problems. An experimental framework is developed in order to test the proposed parallel framework. Particularly, the framework is tested using real-world data belonging to the TSAP and PSP problem. Furthermore, new insights are obtained about modeling these problems as parallel Multi-objective evolutionary Algorithms.

关键词： parallel evolutionary computation Multi-objective Optimization Protein Structure Prediction Task based Sailor Assignment Problem

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