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Titlebook: Nature Inspired Cooperative Strategies for Optimization (NICSO 2008); Natalio Krasnogor,María Belén Melián-Batista,David Book 2009 Springe

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發(fā)表于 2025-3-21 18:08:36 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱Nature Inspired Cooperative Strategies for Optimization (NICSO 2008)
編輯Natalio Krasnogor,María Belén Melián-Batista,David
視頻videohttp://file.papertrans.cn/663/662004/662004.mp4
概述Presents latest results in nature inspired cooperative strategies for optimization
叢書名稱Studies in Computational Intelligence
圖書封面Titlebook: Nature Inspired Cooperative Strategies for Optimization (NICSO 2008); Natalio Krasnogor,María Belén Melián-Batista,David Book 2009 Springe
描述The inspiration from Biology and the Natural Evolution process has become a research area within computer science. For instance, the description of the arti?cial neuron given by McCulloch and Pitts was inspired from biological observations of neural mechanisms; the power of evolution in nature in the diverse species that make up our world has been related to a particular form of problem solving based on the idea of survival of the ?ttest; similarly, - ti?cial immune systems, ant colony optimisation, automated self-assembling programming, membrane computing, etc. also have their roots in natural phenomena. The ?rst and second editions of the International Workshop on Nature Inspired Cooperative Strategies for Optimization (NICSO), were held in Granada, Spain, 2006, and in Acireale, Italy, 2007, respectively. As in these two previous editions, the aim of NICSO 2008, held in Tenerife, Spain, was to provide a forum were the latest ideas and state of the art research related to nature inspired cooperative strategies for problem solving were discussed. The contributions collected in this book were strictly peer reviewed by at least three members of the international programme committee,
出版日期Book 2009
關(guān)鍵詞Cyc; algorithms; communication; evolution; evolutionary algorithm; genetic algorithm; genetic algorithms; m
版次1
doihttps://doi.org/10.1007/978-3-642-03211-0
isbn_softcover978-3-642-26034-6
isbn_ebook978-3-642-03211-0Series ISSN 1860-949X Series E-ISSN 1860-9503
issn_series 1860-949X
copyrightSpringer-Verlag Berlin Heidelberg 2009
The information of publication is updating

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Nature Inspired Cooperative Strategies for Optimization (NICSO 2008)978-3-642-03211-0Series ISSN 1860-949X Series E-ISSN 1860-9503
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https://doi.org/10.1007/978-3-642-03211-0Cyc; algorithms; communication; evolution; evolutionary algorithm; genetic algorithm; genetic algorithms; m
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Aerodynamic Wing Optimisation Using SOMA Evolutionary Algorithm,ed. We present a modern, high performance global optimisation algorithm, following with real engineering application results represented by set of evolutionary-designed wings which we developed in cooperation with a leading civil aircraft design bureau and manufacturer.
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Discrete Particle Swarm Optimization Algorithm for Data Clustering,th the published results of Basic PSO (B-PSO) algorithm, Genetic Algorithm (GA), Differential Evolution (DE) algorithm and Combinatorial Particle Swarm Optimization (CPSO) algorithm. The performance analysis demonstrates the effectiveness of the proposed algorithm in solving the partitional data clustering problems.
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發(fā)表于 2025-3-23 06:10:37 | 只看該作者
A Simple Distributed Particle Swarm Optimization for Dynamic and Noisy Environments, features as well as provides the optimum result tracking capability in dynamic environments. In this research, the DF1 multimodal dynamic environment generator proposed by Morrison and De Jong is used to evaluate the classic PSO, SDPSO and other two adaptive PSOs.
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