Overview
- Demonstrate the applicability of evolutionary computational techniques to solve scheduling problems
Part of the book series: Studies in Computational Intelligence (SCI, volume 49)
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Table of contents (22 chapters)
Keywords
About this book
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.
Editors and Affiliations
Bibliographic Information
Book Title: Evolutionary Scheduling
Editors: Keshav P. Dahal, Kay Chen Tan, Peter I. Cowling
Series Title: Studies in Computational Intelligence
DOI: https://doi.org/10.1007/978-3-540-48584-1
Publisher: Springer Berlin, Heidelberg
eBook Packages: Engineering, Engineering (R0)
Copyright Information: Springer-Verlag Berlin Heidelberg 2007
Hardcover ISBN: 978-3-540-48582-7Published: 15 February 2007
Softcover ISBN: 978-3-642-08017-3Published: 30 November 2010
eBook ISBN: 978-3-540-48584-1Published: 25 April 2007
Series ISSN: 1860-949X
Series E-ISSN: 1860-9503
Edition Number: 1
Number of Pages: XI, 628
Topics: Artificial Intelligence, Mathematical and Computational Engineering