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检索条件"主题词=Parallel Evolutionary Algorithms"

共 61 条记录，以下是51-60 订阅

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An Imperialistic Strategy Approach to Continuous Global Optimization Problem 16

An Imperialistic Strategy Approach to Continuous Global Opti...

引用

16th International Symposium on Symbolic and Numeric algorithms for Scientific Computing (SYNASC)

作者： Anescu, George Univ Politehn Bucuresti Power Plant Engn Fac Bucharest 060042 Romania

ISBN: (纸本)9781479984480

The paper is introducing the principles of a new global optimization strategy, Imperialistic Strategy (IS), applied to the Continuous Global Optimization Problem (CGOP). Inspired from existing multi-population strategies, like the Island Model (IM) approaches to parallel evolutionary algorithms (EA) and the Imperialistic Competitive Algorithm (ICA), the proposed IS method is considered an optimization strategy for the reason that it can integrate other well-known optimization methods, which in the context are regarded as sub-methods (although in other contexts they are prominent global optimization methods). Four optimization methods were implemented and tested in the roles of sub-methods: Genetic Algorithm (GA) (a floating-point representation variant), Differential Evolution (DE), Quantum Particle Swarm Optimization (QPSO) and Artificial Bee Colony (ABC). The optimization performances of the proposed optimization methods were compared on a test bed of 9 known multimodal optimization problems by applying an appropriate testing methodology. The obtained increased success rates of IS multi-population variants compared to the success rates of the optimization sub-methods run separately, combined with the increased computing efficiencies possible to be perceived for parallel and distributed implementations, demonstrated that IS is a promising approach to CGOP.

关键词： genetic algorithms parallel algorithms quantum computing ABC CGOP DE EA GA ICA IM IS multipopulation variants QPSO artificial bee colony continuous global optimization problem differential evolution distributed implementations floating-point representation variant genetic algorithm imperialistic competitive algorithm imperialistic strategy approach island model multimodal optimization problems multipopulation strategies parallel evolutionary algorithms parallel implementations quantum particle swarm optimization Biological cells Genetic algorithms Linear programming Optimization Sociology Statistics Vectors Artificial Bee Colony Continuous Global Optimization Problem Differential Evolution Genetic Algorithm IS Imperialistic Competitive Algorithm Imperialistic Strategy Island Model Quantum Particle Swarm Optimization Genetic Algorithm (GA) MT activity based costing Independent component analysis islet cell antibody Instant messaging Particle swarm optimization Gallium cytology Sociology parallel algorithms quantum computing Policies and plans bee colonies linear programming

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DEAP: A Python Framework for evolutionary algorithms 12

DEAP: A Python Framework for Evolutionary Algorithms

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14th International Conference on Genetic and evolutionary Computation Conference (GECCO)

作者： De Rainville, Francois-Michel Fortin, Felix-Antoine Gardner, Marc-Andre Parizeau, Marc Gagne, Christian Univ Laval Dept Genie Elect & Genie Informat Lab Vis & Syst Numur Quebec City PQ G1V 0A6 Canada

ISBN: (纸本)9781450311786

DEAP (Distributed evolutionary algorithms in Python) is a novel evolutionary computation framework for rapid prototyping and testing of ideas. Its design departs from most other existing frameworks in that it seeks to make algorithms explicit and data structures transparent, as opposed to the more common black box type of frameworks. It also incorporates easy parallelism where users need not concern themselves with gory implementation details like synchronization and load balancing, only functional decomposition. Several examples illustrate the multiple properties of DEAP.

关键词： parallel evolutionary algorithms Software Tools

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Distributed caching strategy

Distributed caching strategy

引用

Conference on Intelligent Computing - Theory and Applications VI

作者： Kim, Keum J. Santos, Eunice E. Santos, Eugene, Jr. Dartmouth Coll Thayer Sch Engn Hanover NH 03755 USA Virginia Polytech Inst & State Univ Comp Sci Dept Blacksburg VA 24061 USA

ISBN: (纸本)9780819471529

When compared to biological experiments, using computational protein models can save time and effort in identifying native conformations of proteins. Nonetheless, given the sheer size of the conformation space, identifying the native conformation remains a computationally hard problem - even in simplified models such as hydrophobic-hydrophilic (HP) models. Distributed systems have become the focus of protein folding, providing high performance computing power to accommodate the conformation space. To use a distributed system efficiently (with limited resources), an appropriate strategy should be designed accordingly. Communication incurs overhead but can provide useful information in distributed systems through careful consideration. Our study focuses on understanding the behavior of distributed systems and developing an efficient communication strategy to save computational effort in order to obtain good solutions. In this paper, we propose a distributed caching strategy, which reuses partial results of computations and transmits the cached and reusable information among neighboring inter-connected processors. In order to validate this idea in a practical setting, we present algorithms to retrieve and restore the cached information and apply them to 2D triangular HP lattice models through coarse-grained parallel genetic algorithms (CPGAs). Our experimental results demonstrate the time savings as well as the limits in caching improvements for our distributed caching strategy.

关键词： protein folding triangular lattice HP model caching stochastic methods distributed systems parallel evolutionary algorithms coarse-grained parallel genetic algorithms migration distributed caching

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An Investigation of Topologies and Migration Schemes for Asynchronous Distributed evolutionary algorithms

An Investigation of Topologies and Migration Schemes for Asy...

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World Congress on Nature and Biologically Inspired Computing

作者： Hijaze, Muhannad Corne, David Heriot Watt Univ Sch MACS Edinburgh Midlothian Scotland

ISBN: (纸本)9781424450534

Distributed evolutionary algorithms are of increasing interest and importance for three main reasons: (i) a well designed distributed evolutionary algorithm (dEA) can outperform a 'standard' EA in terms of reliability, solution quality, and speed;(ii) they can (of course) be implemented on parallel hardware, and hence combine efficient utilization of parallel resources with very fast and reliable optimization;(iii) parallel hardware resources are increasingly common. A dEA operates as separate evolving populations with occasional interaction between them via 'migration'. A specific dEA is characterized by the topology and nature of these interactions. Although the field is sizeable, there is still relatively little exploration of the performance of alternative topologies and interaction mechanisms. In this paper we compare some simple, novel dEA topologies with the cube-based topology that forms the basis of Alba et al's GD-RCGA (a state of the art dEA). We find the best results (when topologies are compared on a like for like basis in terms of number of processors) emerge from a three-level tree-based topology.

关键词： function optimization evolutionary algorithms parallel evolutionary algorithms

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Flock-based architecture for distributed evolutionary algorithms

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7th International Conference on Artificial Intelligence and Soft Computing

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

ISBN: (纸本)3540221239

The paper presents an agent-based architecture facilitating implemetation of parallel evolutionary algorithms, utilising the novel concept of a flock. The model proposed is an extension to classical regional parallel evolutionary algorithm. Flocks introduce additional level of organisation of the system, allowing for separation of distribution and evolution issues, and thus opening possibility of dynamic reconfiguration of subpopulations adequately to the structure of the problem being solved. Selected experimental results illustrate the idea "at work".

关键词： parallel evolutionary algorithms multi-agent systems

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A Software Platform for evolutionary Computation with Pluggable parallelism and Quality Assurance

A Software Platform for Evolutionary Computation with Plugga...

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12th INNS EANN-SIG International Conference (EANN 2011)/7th IFIP 12 5 International Conference (AIAI 2011)

作者： Evangelista, Pedro Pinho, Jorge Goncalves, Emanuel Maia, Paulo Sobral, Joao Luis Rocha, Miguel Univ Minho CCTC Dept Informat P-4710057 Braga Portugal

ISBN: (纸本)9783642239601;9783642239595

This paper proposes the Java evolutionary Computation Library (JECoLi), an adaptable, flexible, extensible and reliable software framework implementing metaheuristic optimization algorithms, using the Java programming language. JECoLi aims to offer a solution suited for the integration of evolutionary Computation (EC)-based approaches in larger applications, and for the rapid and efficient benchmarking of EC algorithms in specific problems. Its main contributions are (i) the implementation of pluggable parallelization modules, independent from the EC algorithms, allowing the programs to adapt. to the available hardware resources in a transparent way, without. changing the base code;(ii) a flexible platform for software quality assurance that allows creating tests for the implemented features and for user-defined extensions. The library is freely available as an open-source project.

关键词： evolutionary Computation Open-source software parallel evolutionary algorithms Software Quality

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Load-Balancing of a Master-Slave evolutionary Algorithm for Parameter Identification

Load-Balancing of a Master-Slave Evolutionary Algorithm for ...

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1st International Conference on parallel, Distributed and Grid Computing for Engineering

作者： Leps, M. Czech Tech Univ Fac Civil Engn Dept Mech CR-16635 Prague Czech Republic

ISBN: (纸本)9781905088294

The goal of this paper is to investigate under which settings it is effective to use an asynchronous version of the master-slave model of a population-based optimization algorithm. Both versions, synchronous and asynchronous, are implemented and compared using data from the multi-objective identification of parameters for a material constitutive law for concrete. Results have shown that the synchronous version is preferable for the low number of processors whereas for the high number of available CPUs the asynchronous version is clear winner.

关键词： optimization parallel evolutionary algorithms load-balancing parameters identification inverse analysis parallel computing

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ESS-IM applied to Forest Fire Spread Prediction: Parameters Tuning for a Heterogeneous Configuration 35

ESS-IM applied to Forest Fire Spread Prediction: Parameters ...

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35th International Conference of the Chilean-Computer-Science-Society (SCCC)

作者： Mendez-Garabetti, Miguel Bianchini, German Caymes-Scutari, Paola Tardivo, Maria Laura Univ Tecnol Nacl Lab Invest Comp Paralelo Distribuido LICPaD Dept Ingn Sistemas Informac Fac Reg Mendoza M5502AJE Mendoza Argentina Consejo Nacl Invest Cient & Tecn Buenos Aires DF Argentina Univ Nacl Rio Cuarto Dept Comp X5804BYA Cordoba Spain

ISBN: (纸本)9781509033393

Forest fires are a critical natural hazard in many regions of the World. For this reason, the prediction of this kind of phenomenon is considered a very important task that involves a high degree of complexity and precision. The ability to predict the forest fire behaviour constitutes an important tool for managers, helping to improve the effectiveness of fire prevention, detection and firefighting resources allocation. For this reason, prediction methods should be configured to operate as efficiently as possible. In this paper, a calibration study of evolutionary-Statistical System with Island Model's evolutionary parameters is presented (ESS-IM). ESS-IM is a general-parallel uncertainty reduction method applied to the forest fires spread prediction.

关键词： forest fire spread prediction parallel evolutionary algorithms parameters tuning high performance computing

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GridVis: Visualisation of Island-Based parallel Genetic algorithms 17

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17th European Conference on Applications of evolutionary Computation (EvpApplications)

作者： Lutton, Evelyne Gilbert, Hugo Cancino, Waldo Bach, Benjamin Parrend, Pierre Collet, Pierre INRA UMR GMPA F-78850 Thiverval Grignon France ENSTA ParisTech F-91762 Palaiseau France INRIA Saclay Ile de France AVIZ Team F-91405 Orsay France Strasbourg Univ ICube Lab F-67084 Strasbourg France CS DC UNESCO UniTwin ECCE F-67084 Strasbourg France ECAM Strasbourg Europe Schiltigheim F-67012 Strasbourg France

ISBN: (纸本)9783662455234;9783662455227

Island Model parallel genetic algorithms rely on various migration models and their associated parameter settings. A fine understanding of how the islands interact and exchange informations is an important issue for the design of efficient algorithms. This article presents GridVis, an interactive tool for visualising the exchange of individuals and the propagation of fitness values between islands. We performed several experiments on a grid and on a cluster to evaluate GridVis' ability to visualise the activity of each machine and the communication flow between machines. Experiments have been made on the optimisation of a Weierstrass function using the EASEA language, with two schemes: a scheme based on uniform islands and another based on specialised islands (Exploitation, Exploration and Storage Islands).

关键词： parallel evolutionary algorithms lsland model Visualisation EASEA Grid model

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Multi-UAV Path Planning with parallel Genetic algorithms on CUDA Architecture

Multi-UAV Path Planning with Parallel Genetic Algorithms on ...

引用

Genetic and evolutionary Computation Conference (GECCO)

作者： Cekmez, Ugur Ozsiginan, Mustafa Sahingoz, Ozgur Koray Yildiz Tech Univ Dept Comp Engn Istanbul Turkey Turkish Air Force Acad Aeronaut & Space Technol Inst Istanbul Turkey Turkish Air Force Acad Dept Comp Engn Istanbul Turkey

ISBN: (纸本)9781450343237

In recent years, the use of Unmanned Aerial Vehicles (UAVs) has grown quickly due to its low cost and easily programming for autonomous path following for accomplishing different types of missions. Due to the numerous advantages of multi-UAVs, when comparing with a single powerful one, to perform reconnaissance, monitoring, detection and surveying missions the use of multi-UAVs is generally preferred. While the number of control points and the number of UAVs are increased, the complexity of the problem also increases. This paper presents a solution to the problem of minimum time coverage of ground areas using a number of UAVs. The solution is divided into two parts: Firstly the area is partitioned with K-means clustering and then the problem is solved in each cluster with parallel genetic algorithm approach on CUDA architecture. To illustrate the methodology, the paper presents the experimental results obtained with a multi-UAV system, which has a different number of control points. The results showed the proposed approach produces efficient solutions for these type NP-Hard problems of homeland security applications like wide-area surveillance and site security by using multiple UAVs.

关键词： parallel evolutionary algorithms K-Means Clustering 2-opt Multi-UAV Path Planning Genetic algorithms

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