Genetic Algorithms: The Design of Innovation
This book illustrates how to design and implement scalable genetic algorithms that solve hard problems quickly, reliably, and accurately. This revised edition includes recent results and new groundbreaking material. The book combines two decades of hard-won research results in a single volume to provide a step-by-step guide to designing genetic algorithms that scale well with problem size and difficulty. A major new chapter demonstrates practical scalability of GAs on a problem with over a billion variables, and shows how these results can be used to obtain routine solutions to important problems. This book is an essential reference.
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Produktdetails
Weitere Autoren: Sastry, Kumara / Goldberg, D.
- ISBN: 978-0-387-35374-6
- EAN: 9780387353746
- Produktnummer: 2631566
- Verlag: Springer Nature
- Sprache: Englisch
- Erscheinungsjahr: 2010
- Seitenangabe: 350 S.
- Masse: H23.5 cm x B15.5 cm
- Auflage: 2. A.
Über den Autor
David E. Goldberg (BSE, 1975, MSE, 1976, PhD, 1983 in Civil Engineering from the University of Michigan, Ann Arbor) is a Professor of General Engineering at the University of Illinois at Urbana-Champaign (UIUC) and director of the Illinois Genetic Algorithms Laboratory (IlliGAL, http://www-illigal.ge.uiuc.edu/). Between 1976 and 1980 he held a number of positions at Stoner Associates of Carlisle, PA, including Project Engineer and Marketing Manager. Following his doctoral studies he joined the Engineering Mechanics faculty at the University of Alabama, Tuscaloosa, in 1984 and he moved to the University of Illinois in 1990. Professor Goldberg was a 1985 recipient of a U.S. National Science Foundation Presidential Young Investigator Award, and in 1995 he was named an Associate of the Center for Advanced Study at UIUC. He was founding chairman of the International Society for Genetic and Evolutionary Computation (http://www.isgec.org/), and his book Genetic Algorithms in Search, Optimization and Machine Learning (Addison-Wesley, 1989) is widely used and cited. His research focuses on the design, analysis, and application of genetic algorithms-computer procedures based on the mechanics of natural genetics and selection-and other innovating machines.
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