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16th Genetic and evolutionary Computation Conference (GECCO)

作者： Mambrini, Andrea Sudholt, Dirk Univ Birmingham Sch Comp Sci Birmingham B15 2TT W Midlands England Univ Sheffield Dept Comp Sci Sheffield S1 4DP S Yorkshire England

ISBN: (纸本)9781450326629

The migration interval is one of the fundamental parameters governing the dynamic behaviour of island models. Yet, there is little understanding on how this parameter affects performance, and how to optimally set it given a problem in hand. We propose schemes for adapting the migration interval according to whether fitness improvements have been found. As long as no improvement is found, the migration interval is increased to minimise communication. Once the best fitness has improved, the migration interval is decreased to spread new best solutions more quickly. We provide a method for analysing the expected running time and the communication effort, defined as the expected number of migrants sent. Example applications of this method to common example functions show that our adaptive schemes are able to compete with, or even outperform, the optimal fixed choice of the migration interval, with regard to running time and communication effort.

关键词： parallel evolutionary algorithms island model migration interval runtime analysis theory

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Runtime Analysis of Unbalanced Block-parallel evolutionary algorithms 17th

Runtime Analysis of Unbalanced Block-Parallel Evolutionary A...

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17th International Conference on parallel Problem Solving from Nature (PPSN)

作者： Aboutaib, Brahim Sutton, Andrew M. Univ Littoral Cote dOpale LISIC F-62100 Calais France Mohammed V Univ Rabat Fac Sci LRIT Rabat Morocco Univ Minnesota Duluth Dept Comp Sci Duluth MN 55812 USA

ISBN: (纸本)9783031147210;9783031147203

We revisit the analysis of the (1+lambda) EA in a parallel setting when the offspring population size is significantly larger than the number of processors available. If the workload is not balanced across the processors, existing runtime results do not transfer directly. We therefore consider two new scenarios that produce unbalanced processors: (1) when the computation time of the fitness function is variable and depends on the structure of the individual, and (2) when processing is interrupted as soon as a viable offspring is found on one of the machines. We derive parallel execution times for both these models as a function of both the population size and the number of parallel machines. We discuss the potential trade-off between communication overhead and execution time, and we conduct some experiments.

关键词： parallel evolutionary algorithms Runtime analysis

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Adaptive Population Models for Offspring Populations and parallel evolutionary algorithms 11

Adaptive Population Models for Offspring Populations and Par...

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11th Foundations of Genetic algorithms Workshop

作者： Laessig, Joerg Sudholt, Dirk Univ Lugano ICS CH-6906 Lugano Switzerland

ISBN: (纸本)9781450306331

We present two adaptive schemes for dynamically choosing the number of parallel instances in parallel evolutionary algorithms. This includes the choice of the offspring population size in a (1-lambda) EA as a special case. Our schemes are parameterless and they work in a black-box setting where no knowledge on the problem is available. Both schemes double the number of instances in case a generation ends without finding an improvement. In a successful generation, the first scheme resets the system to one instance, while the second scheme halves the number of instances. Both schemes provide near-optimal speed-ups in terms of the parallel time. We give upper bounds for the asymptotic sequential time (i. e., the total number of function evaluations) that are not larger than upper bounds for a corresponding non-parallel algorithm derived by the fitness-level method.

关键词： parallel evolutionary algorithms dynamic population size island model spatial structures offspring populations runtime analysis

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Agent-based models and platforms for parallel evolutionary algorithms

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4th International Conference on Computational Science (ICCS 2004)

作者： Kisiel-Dorohinicki, M AGH Univ Sci & Technol Inst Comp Sci Krakow Poland

ISBN: (纸本)3540221166

The goal of the paper is to provide an overview of classical and agent-based models of parallel evolutionary algorithms. Agent approach reveals possibilities of unification of various models and thus allows for the development of platforms supporting the implementation of different PEA variants. Design considerations based on AgWorld and *** projects conclude the paper.

关键词： parallel evolutionary algorithms evolution in multi-agent systems

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Observations in using parallel and sequential evolutionary algorithms for automatic software testing

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COMPUTERS & OPERATIONS RESEARCH 2008年第10期35卷 3161-3183页

作者： Alba, Enrique Chicano, Francisco Univ Malaga Dept Lenguajes & Ciencias Computac Grp GISUM E-29071 Malaga Spain

In this paper we analyze the application of parallel and sequential evolutionary algorithms (EAs) to the automatic test data generation problem. The problem consists of automatically creating a set of input data to test a program. This is a fundamental step in software development and a time consuming task in existing software companies. Canonical sequential EAs have been used in the past for this task. We explore here the use of parallel EAs. Evidence of greater efficiency, larger diversity maintenance, additional availability of memory/CPU, and multi-solution capabilities of the parallel approach, reinforce the importance of the advances in research with these algorithms. We describe in this work how canonical genetic algorithms (GAs) and evolutionary strategies (ESs) can help in software testing, and what the advantages are (if any) of using decentralized populations in these techniques. In addition, we study the influence of some parameters of the proposed test data generator in the results. For the experiments we use a large benchmark composed of twelve programs that includes fundamental algorithms in computer science. (C) 2007 Elsevier Ltd. All rights reserved.

关键词： software testing evolutionary algorithins evolutionary testing parallel evolutionary algorithms

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A unified view of parallel multi-objective evolutionary algorithms

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JOURNAL OF parallel AND DISTRIBUTED COMPUTING 2019年 133卷 349-358页

作者： Talbi, EI-Ghazali Univ Lille 1 Bat M3 F-59655 Villeneuve Dascq France

This paper describes a unified view of parallel evolutionary algorithms for multi-objective optimization problems. The parallel optimization algorithms are detailed from both design and implementation aspects. The proposed taxonomy is based on three hierarchical parallel models. Moreover, various parallel architectures are taken into account. The performance assessment issue of parallel multi-objective evolutionary algorithms (MOEA) is also presented. This work can be extended to any population-based metaheuristics such as particle swarm and scatter search. (C) 2018 Published by Elsevier Inc.

关键词： parallel evolutionary algorithms Metaheuristics Multi-objective optimization

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Massively parallel Simulated Annealing and Its Relation to evolutionary algorithms

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evolutionary COMPUTATION 1993年第4期1卷 361-383页

作者： Rudolph, Guenter Univ Dortmund Fachbereich Informat Lehrstuhl Syst Anal D-44221 Dortmund Germany

Simulated annealing and single-trial versions of evolution strategies possess a close relationship when they are designed for optimization over continuous variables. Analytical investigations of their differences and similarities lead to a cross-fertilization of both approaches, resulting in new theoretical results, new parallel population-based algorithms, and a better understanding of the intcrrelationships.

关键词： global optimization parallel simulated annealing parallel evolutionary algorithms neighborhood algorithms

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Clustering Based parallel Many-Objective evolutionary algorithms Using the Shape of the Objective Vectors 8

Clustering Based Parallel Many-Objective Evolutionary Algori...

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8th International Conference on evolutionary Multi-Criterion Optimization (EMO)

作者： von Luecken, Christian Brizuela, Carlos Baran, Benjamin Univ Nacl Asuncion Fac Politecn San Lorenzo Paraguay CICESE Res Ctr Ensenada Baja California Mexico

ISBN: (纸本)9783319158921;9783319158914

Multi-objective evolutionary algorithms (MOEA) are used to solve complex multi-objective problems. As the number of objectives increases, Pareto-based MOEAs are unable to maintain the same effectiveness showed for two or three objectives. Therefore, as a way to ameliorate this performance degradation several authors proposed preference-based methods as an alternative to Pareto based approaches. On the other hand, parallelization has shown to be useful in evolutionary optimizations. A central aspect for the parallelization of evolutionary algorithms is the population partitioning approach. Thus, this paper presents a new parallelization approach based on clustering by the shape of objective vectors to deal with many-objective problems. The proposed method was compared with random and k-means clustering approaches using a multi-threading framework in parallelization of the NSGA-II and six variants using preference-based relations for fitness assignment. Executions were carried-out for the DTLZ problem suite, and the obtained solutions were compared using the generational distance metric. Experimental results show that the proposed shape-based partition achieves competitive results when comparing to the sequential and to other partitioning approaches.

关键词： Multi-Objective evolutionary algorithms Many-objective optimization parallel evolutionary algorithms

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The Speciating Island Model: An alternative parallel evolutionary algorithm

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JOURNAL OF parallel AND DISTRIBUTED COMPUTING 2006年第8期66卷 1025-1036页

作者： Gustafson, Steven Burke, Edmund K. GE Global Res Comp & Decis Sci Niskayuna NY 12309 USA Univ Nottingham Sch Comp Sci & IT Nottingham NG8 1BB England

This paper presents an investigation of a novel model for parallel evolutionary algorithms (EAs) based on the biological concept of species. In EA population search, new species represent solutions that could lead to good solutions but are disadvantaged due to their dissimilarity from the rest of the population. The Speciating Island Model (SIM) attempts to exploit new species when they arise by allocating them to new search processes executing on other islands (other processors). The long term goal of the SIM is to allow new species to diffuse throughout a large (conceptual) parallel computer network, where idle and unimproving processors initiate a new search process with them. In this paper, we focus on the successful identification and exploitation of new species and show that the SIM can achieve improved solution quality as compared to a canonical parallel EA. (C) 2006 Elsevier Inc. All rights reserved.

关键词： parallel evolutionary algorithms genetic programming islands

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A Delayed Start parallel evolutionary Algorithm for just-in-time truck scheduling at a cross-docking facility

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INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS 2019年 212卷 236-258页

作者： Dulebenets, Maxim A. Florida State Univ Florida A&M Univ Dept Civil & Environm Engn 2525 Pottsdamer StBldg ASuite A124 Tallahassee FL 32310 USA

Nowadays, supply chain stakeholders have to successfully address a large number of challenges in order to ensure an efficient distribution of products within supply chains. These challenges include uncertainties in the supply chain operations, strict delivery deadlines, perishability of the transported products, partnership termination, and others. Cross-docking facilities have been widely used to tackle some of these challenges and improve effectiveness of the product distribution. This study aims to enhance scheduling of the inbound and outbound trucks at a cross-docking facility. The truck scheduling problem is formulated as a mixed integer linear programming model, minimizing the total truck service cost. A novel Delayed Start parallel evolutionary Algorithm is proposed to solve the problem. The algorithm executes separate evolutionary algorithms on its islands in a sequential manner and exchanges the promising solutions among the active islands based on an adaptive migration criterion. The computational experiments showcase superiority of the proposed algorithm in terms of the key algorithmic performance indicators against the other five metaheuristic algorithms, which have been commonly used in the cross-docking literature. Furthermore, this study demonstrates how the developed algorithm can be effectively used for the analysis of important managerial insights from the transportation perspective.

关键词： Supply chain management Cross-docking Truck scheduling Combinatorial optimization Metaheuristics parallel evolutionary algorithms

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