9783662620069 - general-purpose optimization through information maximization di lockett, alan j. (14 risultati)
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Da: GreatBookPricesUK, Woodford Green, Regno UnitoGreatBookPricesUK
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Da: Ria Christie Collections, Uxbridge, Regno UnitoRia Christie Collections
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Da: GreatBookPrices, Columbia, MD, U.S.A.GreatBookPrices
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EUR 243,29
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Da: Books Puddle, New York, NY, U.S.A.Books Puddle
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Da: GreatBookPricesUK, Woodford Green, Regno UnitoGreatBookPricesUK
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EUR 248,86
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Da: AHA-BUCH GmbH, Einbeck, GermaniaAHA-BUCH GmbH
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EUR 219,37
EUR 65,14 spedizioneSpedito da Germania a U.S.A.Quantità: 1 disponibili
Buch. Condizione: 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 thest…ructure 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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Da: Revaluation Books, Exeter, Regno UnitoRevaluation Books
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EUR 306,20
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Hardcover. Condizione: Brand New. 579 pages. 9.25x6.10x1.25 inches. In Stock.
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Da: Brook Bookstore On Demand, Napoli, NA, ItaliaBrook Bookstore On Demand
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EUR 166,29
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Condizione: new. Questo è un articolo print on demand.
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Da: moluna, Greven, Germaniamoluna
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EUR 180,07
EUR 48,99 spedizioneSpedito da Germania a U.S.A.Quantità: Più di 20 disponibili
Condizione: 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…and is pervasive in informatics.
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Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, GermaniaBuchWeltWeit Ludwig Meier e.K.
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EUR 213,99
EUR 23,00 spedizioneSpedito da Germania a U.S.A.Quantità: 2 disponibili
Buch. Condizione: 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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Da: Majestic Books, Hounslow, Regno UnitoMajestic Books
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EUR 269,38
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Condizione: New. Print on Demand.
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Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germaniabuchversandmimpf2000
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EUR 213,99
EUR 60,00 spedizioneSpedito da Germania a U.S.A.Quantità: 1 disponibili
Buch. Condizione: 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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Da: Biblios, frankfurt am main, HESSE, GermaniaBiblios
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EUR 274,72
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Condizione: New. PRINT ON DEMAND.
