molecular biology, ecology, evolutionary biology, and population genetics, underscoring the exciting "general purpose" nature of genetic algorithms as search methods that can be employed across disciplines.An Introduction to Genetic Algorithms is accessible to students and researchers in any scientific disci...
Genetic algorithms have been applied in a vast number of ways. This discussion is limited to the optimization of a numerical function. Following the convention of computer programs, the problem will be considered to be a minimization. (If you want to maximize, then minimizing the negative of yo...
【预订】An Introduction to Genetic Algorithms 美国库房发货,通常付款后3-5周到货! 作者:Mitchell, Melanie出版社:Bradford Book出版时间:1988年08月 手机专享价 ¥ 当当价 降价通知 ¥822.00 配送至 北京 至 北京市东城区 服务 由“中国进口图书旗舰店”发货,并提供售后服务。
[4]M.Mitchell,AnIntroductiontoGeneticAlgorithms.Cambridge,MA: MITPress,1996. [5]N.J.Radcliffe,“Formaanalysisandrandomrespectfulrecombination,” inProc.4thInt.Conf.GeneticAlgorithms,R.K.BelewandL.B.Booker, Eds.SanMateo,CA:MorganKaufmann,1991,pp.222–229. ...
An Introduction to Genetic Algorithms (MIT Press, 1996). Baumert, T., Brixner, T., Seyfried, V., Strehle, M. & Gerber, G. Femtosecond pulse shaping by an evolutionary algorithm with feedback. Applied Physics B: Lasers and Optics 65, 779–782 (1997). Article ADS CAS Google Scholar ...
In [305], the authors defined genetic algorithms to enhance the versatility of digital quantum simulations. The authors found, that genetic algorithms can be used to increase the fidelity of quantum states, and to optimize the resource requirements of some digital quantum simulation protocols. The ...
One chapter is dedicated to the popular genetic algorithms. This revised edition contains three entirely new chapters on critical topics regarding the pragmatic application of machine learning in industry. The chapters examine multi-label domains, unsupervised learning and its use in deep learning, and...
An optimization perspective on global search methods is featured and includes discussions on genetic algorithms, particle swarm optimization, and the simulated annealing algorithm. In addition, the book includes an elementary introduction to artificial neural networks, convex optimization, and multi-objective...
"The goal of computational genomics is the understanding and interpretation of information encoded and expressed from the entire genetic complement of biological organisms: the genome. This book provides an introduction to the subject, on the level of a senior or first-year graduate-level course, to...
However, the computational limitation to achieve this precise target of tight clusters prohibits it from being used for large microarray gene expression data or any other large data set, which are common nowadays. We propose a pragmatic and scalable version of the tight clustering method that is ...