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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A Strategy for Rapid Quantification of Uncertainty in Reservoir Performance Prediction
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Dynamic opposite learning enhanced teaching–learning-based optimization
TL;DR: Comprehensive numerical results with the comparisons with the state-of-the-art counterparts show that DOLTLBO has significant advantages of converging to the global optimum on most benchmarks and engineering problems, which also validates the superiority of the novel DOL operator.
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Upgraded Whale Optimization Algorithm for fuzzy logic based vibration control of nonlinear steel structure
Mahdi Azizi,Reza Goli Ejlali,Seyyed Arash Mousavi Ghasemi,Siamak Talatahari,Siamak Talatahari +4 more
TL;DR: An Upgraded Whale Optimization Algorithm is proposed and utilized as the optimization technique for parameter tuning of the fuzzy controller and the obtained results prove that the upgraded method is capable of providing competitive results.
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A review of asset management literature on multi-asset systems
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Energy-oriented scheduling based on Evolutionary Algorithms
TL;DR: An energy-oriented scheduling approach for a parallel machine environment using a Genetic Algorithm and two Memetic Algorithms to solve the resource leveling problem for large problems and promising results concerning energy costs and carbon dioxide emissions are reported.
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.