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Titlebook: General-Purpose Optimization Through Information Maximization; Alan J. Lockett Book 2020 Springer-Verlag GmbH Germany, part of Springer Na

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發(fā)表于 2025-3-21 17:33:52 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱General-Purpose Optimization Through Information Maximization
編輯Alan J. Lockett
視頻videohttp://file.papertrans.cn/383/382158/382158.mp4
概述The book will be valuable for researchers in the areas of global optimization, machine learning, evolutionary theory, and control theory.Optimization is a fundamental problem that recurs across scient
叢書名稱Natural Computing Series
圖書封面Titlebook: General-Purpose Optimization Through Information Maximization;  Alan J. Lockett Book 2020 Springer-Verlag GmbH Germany, part of Springer Na
描述.This book examines the mismatch between?discrete programs,?which lie at the center of?modern applied mathematics, and the continuous space phenomena they simulate. The author considers whether we can imagine continuous spaces?of programs, and asks what the?structure of such spaces?would be?and how they would be?constituted. He proposes?a functional analysis?of program spaces focused through the lens of iterative optimization...The author begins with the observation that optimization methods such as Genetic Algorithms, Evolution Strategies, and Particle Swarm Optimization can be analyzed as Estimation of Distributions Algorithms (EDAs) in that they can be formulated as conditional probability distributions. The probabilities themselves are mathematical objects that can be compared and operated on, and thus many methods in Evolutionary Computation can be placed in a shared vector space and analyzed using techniques of functionalanalysis. The core ideas of this book expand from that concept, eventually incorporating all iterative stochastic search methods, including gradient-based methods. Inspired by work on Randomized Search Heuristics, the author covers all iterative optimization
出版日期Book 2020
關(guān)鍵詞Artificial Intelligence; Neuroevolution; Information Maximization; Optimization; Evolutionary Annealing;
版次1
doihttps://doi.org/10.1007/978-3-662-62007-6
isbn_softcover978-3-662-62009-0
isbn_ebook978-3-662-62007-6Series ISSN 1619-7127 Series E-ISSN 2627-6461
issn_series 1619-7127
copyrightSpringer-Verlag GmbH Germany, part of Springer Nature 2020
The information of publication is updating

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發(fā)表于 2025-3-21 22:04:54 | 只看該作者
Book 2020d analyzed using techniques of functionalanalysis. The core ideas of this book expand from that concept, eventually incorporating all iterative stochastic search methods, including gradient-based methods. Inspired by work on Randomized Search Heuristics, the author covers all iterative optimization
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General-Purpose Optimization Through Information Maximization978-3-662-62007-6Series ISSN 1619-7127 Series E-ISSN 2627-6461
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發(fā)表于 2025-3-22 13:33:25 | 只看該作者
Computer Network Architectures and Protocolsodern age without the aid of a digital machine. One fascinating aspect of this observation is that these discrete programs are commonly used to simulate phenomena whose mathematical formulation is based on continuous spaces.
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https://doi.org/10.1007/978-1-4613-0809-6computer, however, analytic solutions and fast-converging iterative methods were the only practical means of performing optimization. The introduction and proliferation of computing technologies widened both the scope and the number of optimization problems.
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Introduction,odern age without the aid of a digital machine. One fascinating aspect of this observation is that these discrete programs are commonly used to simulate phenomena whose mathematical formulation is based on continuous spaces.
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