Proceedings ArticleDOI
On genetic algorithms
Eric B. Baum,Dan Boneh,Charles Garrett +2 more
- pp 230-239
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TLDR
C Culling is near optimal for this problem, highly noise tolerant, and the best known a~~roach in some regimes, and some new large deviation bounds on this submartingale enable us to determine the running time of the algorithm.Abstract:
We analyze the performance of a Genetic Type Algorithm we call Culling and a variety of other algorithms on a problem we refer to as ASP. Culling is near optimal for this problem, highly noise tolerant, and the best known a~~roach . . in some regimes. We show that the problem of learning the Ising perception is reducible to noisy ASP. These results provide an example of a rigorous analysis of GA’s and give insight into when and how C,A’s can beat competing methods. To analyze the genetic algorithm, we view it as a special type of submartingale. We prove some new large deviation bounds on this submartingale w~ich enable us to determine the running time of the algorithm.read more
Citations
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Journal ArticleDOI
Optimization of Laminated Composites by Using Genetic Algorithm and the Polar Description of Plane Anisotropy
TL;DR: In this paper, the authors developed and applied a quite general approach to optimal design of composite laminates where elastic symmetries can also be explicitly expressed as criteria of the optimization process.
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Novel hybrid approach to data-packet-flow prediction for improving network traffic analysis
Bao Rong Chang,Hsiu Fen Tsai +1 more
TL;DR: A novel hybrid approach, combining adaptive neuro-fuzzy inference system (ANFIS) with nonlinear generalized autoregressive conditional heteroscedasticity (NGARCH), is tuned optimally by quantum minimization (QM) and then applied to forecasting the flow of data packets around website.
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Neighborhood rough sets with distance metric learning for feature selection
TL;DR: Zhang et al. as discussed by the authors proposed a neighborhood rough set model based on distance metric learning (NMD) to improve the discriminative ability and decrease the uncertainty in the representation from neighborhood rough sets to deal with this issue.
Proceedings ArticleDOI
Single document extractive text summarization using Genetic Algorithms
TL;DR: The initial results obtained seem promising and encouraging for future work in this area, and are compared with a couple of other existing text summarization methods keeping the DUC2002 data as benchmark, and using the precision-recall evaluation technique.
Journal ArticleDOI
Synthetic shuffling and in vitro selection reveal the rugged adaptive fitness landscape of a kinase ribozyme
Edward A. Curtis,David P. Bartel +1 more
TL;DR: The activity of the most efficient recombinant ribozyme isolated from the synthetically shuffled pool was 30-fold greater than that of any of the ribozymes used to build it, which indicates that synthetic shuffling can be a rich source of ribo enzyme variants with improved properties.
References
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Book
Genetic algorithms in search, optimization, and machine learning
TL;DR: In this article, the authors present the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields, including computer programming and mathematics.
Genetic algorithms in search, optimization and machine learning
TL;DR: This book brings together the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields.
Book
Adaptation in natural and artificial systems
TL;DR: Names of founding work in the area of Adaptation and modiication, which aims to mimic biological optimization, and some (Non-GA) branches of AI.
Book ChapterDOI
Probability Inequalities for sums of Bounded Random Variables
TL;DR: In this article, upper bounds for the probability that the sum S of n independent random variables exceeds its mean ES by a positive number nt are derived for certain sums of dependent random variables such as U statistics.
Book
The Probabilistic Method
TL;DR: A particular set of problems - all dealing with “good” colorings of an underlying set of points relative to a given family of sets - is explored.