Description: General-purpose Optimization Through Information Maximization, Hardcover by Lockett, Alan J., ISBN 3662620065, ISBN-13 9783662620069, Brand New, Free shipping in the US
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.
Th will be valuable for researchers in the areas of global optimization, machine learning, evolutionary theory, and control theory.
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Book Title: General-purpose Optimization Through Information Maximization
Number of Pages: Xviii, 561 Pages
Publication Name: General-Purpose Optimization Through Information Maximization
Language: English
Publisher: Springer Berlin / Heidelberg
Subject: Intelligence (Ai) & Semantics, Computer Science, Optimization
Publication Year: 2020
Type: Textbook
Item Weight: 36.2 Oz
Item Length: 9.3 in
Subject Area: Mathematics, Computers
Author: Alan J. Lockett
Series: Natural Computing Ser.
Item Width: 6.1 in
Format: Hardcover
