Lockett Alan J (27 results)
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Seller: GreatBookPricesUK, Woodford Green, United KingdomGreatBookPricesUK
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Published by Springer, Berlin|Springer Berlin Heidelberg|Springer, 2021
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Taschenbuch. Condition: Neu. General-Purpose Optimization Through Information Maximization | Alan J. Lockett | Taschenbuch | Natural Computing Series | xviii | Englisch | 2021 | Springer | EAN 9783662620090 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]spr…inger[dot]com | Anbieter: preigu.
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Seller: GreatBookPricesUK, Woodford Green, United KingdomGreatBookPricesUK
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Condition: New. 1st ed. 2020 edition NO-PA16APR2015-KAP.
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Buch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book examines the mismatch betweendiscrete programs,which lie at the center ofmodern applied mathematics, and the continuous space phenomena they simulate. The author considers whether we can imagine continuous spacesof programs, and asks what thestr…ucture of such spaceswould beand how they would beconstituted. He proposesa functional analysisof 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 methods and not just evolutionary methods. The No Free Lunch Theorem is viewed as a useful introduction to the broader field of analysis that comes from developing a shared mathematical space for optimization algorithms. The author brings in intuitions from several branches of mathematics such as topology, probability theory, and stochastic processes and provides substantial background material to make the work as self-contained as possible.The book will be valuable for researchers in the areas of global optimization, machine learning, evolutionary theory, and control theory.
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Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
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Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book examines the mismatch betweendiscrete programs,which lie at the center ofmodern applied mathematics, and the continuous space phenomena they simulate. The author considers whether we can imagine continuous spacesof programs, and asks what… thestructure of such spaceswould beand how they would beconstituted. He proposesa functional analysisof 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 methods and not just evolutionary methods. The No Free Lunch Theorem is viewed as a useful introduction to the broader field of analysis that comes from developing a shared mathematical space for optimization algorithms. The author brings in intuitions from several branches of mathematics such as topology, probability theory, and stochastic processes and provides substantial background material to make the work as self-contained as possible.The book will be valuable for researchers in the areas of global optimization, machine learning, evolutionary theory, and control theory.
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Paperback. Condition: Brand New. 579 pages. 9.25x6.10x1.17 inches. In Stock.
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Seller: Revaluation Books, Exeter, United KingdomRevaluation Books
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Condition: Brand New. New. US edition. Print on demand title. Delivery takes 20-25 days. Excellent Customer Service.
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Seller: Brook Bookstore On Demand, Napoli, NA, ItalyBrook Bookstore On Demand
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Seller: moluna, Greven, Germanymoluna
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Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. The book will be valuable for researchers in the areas of global optimization, machine learning, evolutionary theory, and control theoryOptimization is a fundamental problem that recurs across scientific disciplines a…nd is pervasive in informatics.
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Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, GermanyBuchWeltWeit Ludwig Meier e.K.
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book examines the mismatch betweendiscrete programs,which lie at the center ofmodern applied mathematics, and the continuous space phenomena they simulate. The author considers whether we can imagine continuous spacesof program…s, and asks what thestructure of such spaceswould beand how they would beconstituted. He proposesa functional analysisof 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 functional analysis. 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 methods and not just evolutionary methods. The No Free Lunch Theorem is viewed as a useful introduction to the broader field of analysis that comes from developing a shared mathematical space for optimization algorithms. The author brings in intuitions from several branches of mathematics such as topology, probability theory, and stochastic processes and provides substantial background material to make the work as self-contained as possible.The book will be valuable for researchers in the areas of global optimization, machine learning, evolutionary theory, and control theory. 580 pp. Englisch.
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Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, GermanyBuchWeltWeit Ludwig Meier e.K.
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Buch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book examines the mismatch betweendiscrete programs,which lie at the center ofmodern applied mathematics, and the continuous space phenomena they simulate. The author considers whether we can imagine continuous spacesof programs, and…asks what thestructure of such spaceswould beand how they would beconstituted. He proposesa functional analysisof 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 functional analysis. 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 methods and not just evolutionary methods. The No Free Lunch Theorem is viewed as a useful introduction to the broader field of analysis that comes from developing a shared mathematical space for optimization algorithms. The author brings in intuitions from several branches of mathematics such as topology, probability theory, and stochastic processes and provides substantial background material to make the work as self-contained as possible.The book will be valuable for researchers in the areas of global optimization, machine learning, evolutionary theory, and control theory. 580 pp. Englisch.
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Seller: buchversandmimpf2000, Emtmannsberg, BAYE, Germanybuchversandmimpf2000
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Buch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -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 methods and not just evolutionary methods. The No Free Lunch Theorem is viewed as a useful introduction to the broader field of analysis that comes from developing a shared mathematical space for optimization algorithms. The author brings in intuitions from several branches of mathematics such as topology, probability theory, and stochastic processes and provides substantial background material to make the work as self-contained as possible.The book will be valuable for researchers in the areas of global optimization, machine learning, evolutionary theory, and control theory.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 580 pp. Englisch.
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Seller: buchversandmimpf2000, Emtmannsberg, BAYE, Germanybuchversandmimpf2000
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Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -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 program…s, 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 methods and not just evolutionary methods. The No Free Lunch Theorem is viewed as a useful introduction to the broader field of analysis that comes from developing a shared mathematical space for optimization algorithms. The author brings in intuitions from several branches of mathematics such as topology, probability theory, and stochastic processes and provides substantial background material to make the work as self-contained as possible.The book will be valuable for researchers in the areas of global optimization, machine learning, evolutionary theory, and control theory.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 580 pp. Englisch.
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Seller: Biblios, frankfurt am main, HESSE, GermanyBiblios
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