H.-G. Beyer. Evolutionary algorithms in noisy environments: Theoretical issues and guidelines for practice. Computer Methods in Applied Mechanics and Engineering, 186(2--4):239--267, 2000. Home/Search Document Details and Download
Summary Related Articles Check |
This paper is cited in the following contexts:
Threshold Selection, Hypothesis Tests, and DOE Methods - Beielstein, Markon (Correct)
....a wide spectrum of optimization problems [16] Common means used by evolutionary algorithms to cope with noise are resampling, and adaptation of the population size. Newer approaches use efficient averaging techniques, based on statistical tests, or local regression methods for fitness estimation [3, 1, 17, 6, 15]. In the present paper we concentrate our investigations on the selection process. From our point of view the following case is fundamental for the selection procedure in noisy environments: Reject or accept a new candidate, while the available information is uncertain. Thus, two errors may ....
H.-G. Beyer. Evolutionary algorithms in noisy environments: Theoretical issues and guidelines for practice. CMAME (Computer methods in applied mechanics and engineering), 186:239--267, 2000.
A Partial Order Approach to Noisy Fitness Functions - Rudolph (2001) (1 citation) (Correct)
.... for coping with noisy fitness functions in evolutionary algorithms include the resampling of the random fitness value with averaging, the appropriate adjustment (i.e. enlargement) of the population size, and in case of continuous search spaces also the rescaling of inherited mutations; see Beyer (2000) for a summary of work on EAs for noisy fitness functions. Here, we add yet another avenue for dealing with noisy fitness functions: Instead of using a selection procedure that is based on the totally ordered set of noisy fitness values we endow the probabilistic fitness set with an appropriate ....
H.-G. Beyer (2000). Evolutionary algorithms in noisy environments: Theoretical issues and guidelines for practice.
Some Theoretical Properties of Evolutionary Algorithms under.. - Rudolph (Correct)
.... is best developed currently for the field of optimization of a single deterministic objective function (see e.g. 7] for a survey) There is also a steadily growing theory for EAs facing a (single) stochastically perturbed objective function as can be learned from the overview presented in [1]. In case of multiple objective functions, however, the theory is still in its infancy: Only few results are known [8, 4] The situation is even worse for other problem classes since theoretical results concerning EAs are unknown apparently. This situation may change by the approach initiated in ....
H.-G. Beyer. Evolutionary algorithms in noisy environments: Theoretical issues and guidelines for practice. Computer Methods in Applied Mechanics and Engineering, 186(2-4):239--267, 2000.
A Partial Order Approach to Noisy Fitness Functions - Rudolph (2001) (1 citation) (Correct)
.... for coping with noisy fitness functions in evolutionary algorithms include the resampling of the random fitness value with averaging, the appropriate adjustment (i.e. enlargement) of the population size, and in case of continuous search spaces also the rescaling of inherited mutations; see Beyer (2000) for a summary of work on EAs for noisy fitness functions. Here, we add yet another avenue for dealing with noisy fitness functions: Instead of using a selection procedure that is based on the totally ordered set of noisy fitness values we endow the probabilistic fitness set with an appropriate ....
H.-G. Beyer (2000). Evolutionary algorithms in noisy environments: Theoretical issues and guidelines for practice.
Applying Evolutionary Algorithms to Problems with Noisy, .. - Di Pietro, While, Barone (2004) (Correct)
No context found.
H.-G. Beyer. Evolutionary algorithms in noisy environments: Theoretical issues and guidelines for practice. Computer Methods in Applied Mechanics and Engineering, 186(2--4):239--267, 2000.
Reducing Random Fluctuations in - Mutative Self-Adaptation Thomas (Correct)
No context found.
H.-G. Beyer. Evolutionary algorithms in noisy environments: theoretical issues and guidelines for practice. Computer Methods in Applied Mechanics and Engineering, 186(2--4):239--267, 2000.
Online articles have much greater impact More about CiteSeer.IST Add search form to your site Submit documents Feedback
CiteSeer.IST - Copyright Penn State and NEC