| 书目名称 | General-Purpose Optimization Through Information Maximization | | 编辑 | Alan J. Lockett | | 视频video | | | 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 | | 丛书名称 | Natural Computing Series | | 图书封面 |  | | 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 | | 关键词 | Artificial Intelligence; Neuroevolution; Information Maximization; Optimization; Evolutionary Annealing; | | 版次 | 1 | | doi | https://doi.org/10.1007/978-3-662-62007-6 | | isbn_softcover | 978-3-662-62009-0 | | isbn_ebook | 978-3-662-62007-6Series ISSN 1619-7127 Series E-ISSN 2627-6461 | | issn_series | 1619-7127 | | copyright | Springer-Verlag GmbH Germany, part of Springer Nature 2020 |
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发表于 2025-3-21 17:33:52
