General-Purpose Optimization Through Information Maximization / Najlacnejšie knihy
General-Purpose Optimization Through Information Maximization

Kód: 33086296

General-Purpose Optimization Through Information Maximization

Autor Alan J. Lockett

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 p ... celý popis

205.74

Bežne: 222.86 €

Ušetríte 17.11 €


Skladom u dodávateľa
Odosielame za 10 - 13 dní
Pridať medzi želanie

Mohlo by sa vám tiež páčiť

Darujte túto knihu ešte dnes
  1. Objednajte knihu a vyberte Zaslať ako darček.
  2. Obratom obdržíte darovací poukaz na knihu, ktorý môžete ihneď odovzdať obdarovanému.
  3. Knihu zašleme na adresu obdarovaného, o nič sa nestaráte.

Viac informácií

Viac informácií o knihe General-Purpose Optimization Through Information Maximization

Nákupom získate 498 bodov

Anotácia knihy

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 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.

Parametre knihy

205.74

Obľúbené z iného súdka



Osobný odber Bratislava a 12825 dalších

Copyright ©2008-26 najlacnejsie-knihy.sk Všetky práva vyhradenéSúkromieCookies


Môj účet: Prihlásiť sa
Všetky knihy sveta na jednom mieste. Navyše za skvelé ceny.

Nákupný košík ( prázdny )

Vyzdvihnutie v Zásielkovni
zadarmo nad 59,99 €.

Nachádzate sa: