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Theory of Evolutionary Computation: Recent Developments in Discrete Optimization 2020 Edition
Contributor(s): Doerr, Benjamin (Editor), Neumann, Frank (Editor)
ISBN: 3030294137     ISBN-13: 9783030294137
Publisher: Springer
OUR PRICE:   $237.49  
Product Type: Hardcover - Other Formats
Published: December 2019
Qty:
Additional Information
BISAC Categories:
- Computers | Computer Science
- Computers | Intelligence (ai) & Semantics
- Mathematics | Applied
Dewey: 004.015
Series: Natural Computing
Physical Information: 1.13" H x 6.14" W x 9.21" (2.02 lbs) 506 pages
 
Descriptions, Reviews, Etc.
Publisher Description:

This edited book reports on recent developments in the theory of evolutionary computation, or more generally the domain of randomized search heuristics.

It starts with two chapters on mathematical methods that are often used in the analysis of randomized search heuristics, followed by three chapters on how to measure the complexity of a search heuristic: black-box complexity, a counterpart of classical complexity theory in black-box optimization; parameterized complexity, aimed at a more fine-grained view of the difficulty of problems; and the fixed-budget perspective, which answers the question of how good a solution will be after investing a certain computational budget. The book then describes theoretical results on three important questions in evolutionary computation: how to profit from changing the parameters during the run of an algorithm; how evolutionary algorithms cope with dynamically changing or stochastic environments; and how population diversity influences performance. Finally, the book looks at three algorithm classes that have only recently become the focus of theoretical work: estimation-of-distribution algorithms; artificial immune systems; and genetic programming.

Throughout the book the contributing authors try to develop an understanding for how these methods work, and why they are so successful in many applications. The book will be useful for students and researchers in theoretical computer science and evolutionary computing.