Evolutionary Scheduling (Studies in Computational Intelligence)

Dahal, Keshav (Editor) / Tan, Kay Chen (Editor) / Cowling, Peter I. (Editor)

ISBN 10: 3642080170 / ISBN 13: 9783642080173

Published by Springer Berlin Heidelberg, 2007

About this Item

628 pages. 9.00x6.00x1.45 inches. In Stock.

Seller Inventory # x-3642080170

Bibliographic Details

• Title: Evolutionary Scheduling (Studies in Computational Intelligence)
• Publisher: Springer Berlin Heidelberg
• Publication Date: 2007
• Language: English
• ISBN 10: 3642080170
• ISBN 13: 9783642080173
• Binding: Paperback
• Condition: Brand New
• Weight: 0.97 kilograms

About this title

Synopsis

Evolutionary scheduling is a vital research domain at the interface of artificial intelligence and operational research. This edited book gives an overview of many of the current developments in the large and growing field of evolutionary scheduling. It demonstrates the applicability of evolutionary computational techniques to solve scheduling problems, not only to small-scale test problems, but also fully-fledged real-world problems.

From the Back Cover

Evolutionary scheduling is a vital research domain at the interface of two important sciences - artificial intelligence and operational research. Scheduling problems are generally complex, large scale, constrained, and multi-objective in nature, and classical operational research techniques are often inadequate at solving them effectively. With the advent of computation intelligence, there is renewed interest in solving scheduling problems using evolutionary computational techniques. These techniques, which include genetic algorithms, genetic programming, evolutionary strategies, memetic algorithms, particle swarm optimization, ant colony systems, etc, are derived from biologically inspired concepts and are well-suited to solve scheduling problems since they are highly scalable and flexible in terms of handling constraints and multiple objectives. This edited book gives an overview of many of the current developments in the large and growing field of evolutionary scheduling, and demonstrates the applicability of evolutionary computational techniques to solve scheduling problems, not only to small-scale test problems, but also fully-fledged real-world problems. The intended readers of this book are engineers, researchers, practitioners, senior undergraduates, and graduate students who are interested in the field of evolutionary scheduling.