Analysis of Noisy Evolutionary Optimization When Sampling Fails
نویسندگان
چکیده
منابع مشابه
Sequential Sampling in Noisy Multi-Objective Evolutionary Optimization
Most real-world optimization problems behave stochastically. Evolutionary optimization algorithms have to cope with the uncertainty in order to not loose a substantial part of their performance. There are different types of uncertainty and this thesis studies the type that is commonly known as noise and the use of resampling techniques as countermeasure in multi-objective evolutionary optimizat...
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In real-world optimization tasks, the objective (i.e., fitness) function evaluation is often disturbed by noise due to a wide range of uncertainties. Evolutionary algorithms are often employed in noisy optimization, where reducing the negative effect of noise is a crucial issue. Sampling is a popular strategy for dealing with noise: to estimate the fitness of a solution, it evaluates the fitnes...
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Many optimization tasks must be handled in noisy environments, where the exact evaluation of a solution cannot be obtained, only a noisy one. For optimization of noisy tasks, evolutionary algorithms (EAs), a type of stochastic metaheuristic search algorithm, have been widely and successfully applied. Previous work mainly focuses on the empirical study and design of EAs for optimization under no...
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ژورنال
عنوان ژورنال: Algorithmica
سال: 2020
ISSN: 0178-4617,1432-0541
DOI: 10.1007/s00453-019-00666-6