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Genetic Algorithms
The Design of Innovation
ISBN/EAN: 9780387353746
Umbreit-Nr.: 1558518
Sprache:
Englisch
Umfang: XIV, 350 S., 15 s/w Fotos
Format in cm:
Einband:
gebundenes Buch
Erschienen am 13.01.2021
Auflage: 2/2021
- Zusatztext
- InhaltsangabeList of Figures.- List of Tables.- Preface.- Acknowledgments.- Genetic Algorithms and Innovation.- Making Genetic Algorithms Fly.- Three Tools of Conceptual Engineering.- Goals and Elements of GA Design.- Understanding Building Blocks.- A Design Approach to Problem Difficulty.- Ensuring Building Block Supply.- Ensuring Building Block Growth.- Making Time for Building Blocks.- Deciding Well.- Mixing, Control Maps, and GA Success.- Designing Scalable Genetic Algorithms.- Principled Efficiency Enhancement Techniques.- A Billion Variables and Beyond.- Cool Technology, Philosophical Reflection, and Conscious Computation.- References.- Index.
- Kurztext
- This book illustrates how to design and implement scalable genetic algorithms that solve hard problems quickly, reliably, and accurately. This revised edition of the landmark The Design of Innovation includes recent results and new groundbreaking material.
- Autorenportrait
- 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.