Articles liés à Genetic Algorithms: The Design of Innovation
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Synopsis
The first edition of this book (Goldberg, 2002) was welcomed as an important contribution to the understanding and design of scalable genetic algorithms. Goldberg's theory of facetwise models proves invaluable to GA understanding and design, and the core chapters of the book continue to make those important arguments; however, they are brought up to date with the most important recent results, including population timing and sizing results. The first edition was an important landmark in the theory and practice of genetic algorithms, and problem size and difficulty of problem tackled has progressed rapidly since its publication. Genetic Algorithms: The Design of Innovation (2nd Edition) updates that text with important additions, new groundbreaking material, and important suggestions for key research directions and likely lines of successful inquiry.
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À propos de l?auteur
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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