General-Purpose Optimization Through Information Maximization
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 progra
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Alan J. Lockett
General-Purpose Optimization Through Information Maximization
Natural Computing Series Series Editors: Thomas Bäck
Lila Kari
Natural Computing is one of the most exciting developments in computer science, and there is a growing consensus that it will become a major field in this century. This series includes monographs, textbooks, and state-of-the-art collections covering the whole spectrum of Natural Computing and ranging from theory to applications. More information about this series at http://www.springer.com/series/4190
Alan J. Lockett
General-Purpose Optimization Through Information Maximization
Alan J. Lockett CS Disco Inc. Austin, TX, USA
ISSN 1619-7127 Natural Computing Series ISBN 978-3-662-62006-9 ISBN 978-3-662-62007-6 (eBook) https://doi.org/10.1007/978-3-662-62007-6 © Springer-Verlag GmbH Germany, part of Springer Nature 2020 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer-Verlag GmbH, DE part of Springer Nature. The registered company address is: Heidelberger Platz 3, 14197 Berlin, Germany
Dedicated to my wife Alliene, who patiently and devotedly endured her husband’s late night dalliance with abstract mathematics.
Preface
This book began out of observations upon reviewing the literature around Estimation of Distributions Algorithms (EDAs) for optimization and recognizing that there was nothing particularly special or different about these algorithms from other methods such as Genetic Algorithms, Evolution Strategies, and Particle Swarm Optimization. It seemed obvious to me that each of these could be formulated as a kind of conditional probability distribution in the same manner as EDAs, perhaps with some degeneracies incorporated. Furthermore, these conditional probabilities were themselves mathematical objects that could be compared and op
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