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
Lévy flight distribution: A new metaheuristic algorithm for solving engineering optimization problems
TL;DR: The statistical simulation results revealed that the LFD algorithm provides better results with superior performance in most tests compared to several well-known metaheuristic algorithms such as simulated annealing (SA), differential evolution (DE), particle swarm optimization (PSO), elephant herding optimization (EHO), the genetic algorithm (GA), moth-flame optimization algorithm (MFO), whale optimization algorithm
Journal ArticleDOI
The Analysis of Evolutionary Algorithms—A Proof That Crossover Really Can Help
Thomas Jansen,Ingo Wegener +1 more
TL;DR: It is proved that an evolutionary algorithm can produce enough diversity such that the use of crossover can speedup the expected optimization time from superpolynomial to a polynomial of small degree.
Journal ArticleDOI
Evolutionary algorithms in noisy environments : theoretical issues and guidelines for practice
TL;DR: The method of rescaled mutations is analyzed in depth for the (1,�)-ES on the sphere model and it is shown that this method needs advanced self-adaptation techniques in order to take advantage of the theoretically predicted performance gain.
Journal ArticleDOI
A survey of techniques for characterising fitness landscapes and some possible ways forward
TL;DR: An overview of techniques from the 1980s to the present is provided, revealing the wide range of factors that can influence problem difficulty and emphasising the need for a shift in focus away from predicting problem hardness towards measuring characteristics.
Journal ArticleDOI
A novel metaheuristic optimizer inspired by behavior of jellyfish in ocean
Jui-Sheng Chou,Dinh-Nhat Truong +1 more
TL;DR: The JS algorithm was used to solve structural optimization problems, including 25- bar tower design and 582-bar tower design problems, where JS not only performed best but also required the fewest evaluations of objective functions.
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.