Thresholding-a selection operator for noisy ES

Markon, Sandor; Arnold, Dirk V.; Bäck, Thomas; Beielstein, Thomas; Beyer, Hans-Georg

doi:10.1109/cec.2001.934428

Cited by 60 publications

(37 citation statements)

References 4 publications

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“…The efficacy of generating offspring is also compared using the progress rate (PR) 33) , which is of the indicators for determining the efficiency of a search method. PR is the expectation value of the progress per one step under a given condition.…”

Section: Numerical Experimentsmentioning

confidence: 99%

Effectiveness of Genetic Multistep Search in Unsupervised Design of Morphological Filters for Noise Removal

Hanada

Muneyasu

Asano

2010

IPSJ Online Transactions

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In this paper, the effectiveness of deterministic Multi-step Crossover Fusion (dMSXF) and deterministic Multi-step Mutation Fusion (dMSMF), which are types of genetic multistep searches based on a neighborhood search mechanism, in solving an unsupervised design problem of suitable structuring elements (SEs) of a morphological filter is shown. In our previous work, it was shown that dMSXF and dMSMF are very effective for solving combinatorial optimization problems, particularly on problems for which the landscape is an AR(1) landscape observed in the NK model. In addition, their effectiveness for reproduction mechanisms to obtain the offspring was shown to be retained with increasing level of epistasis. In this paper, we show that a characteristic of the AR(1) landscape is observed in an objective function for the unsupervised design of SEs, and superior search performances of both dMSXF and dMSMF for conventional crossover are shown. The processing results of the obtained SEs are also compared with those of conventional filters used for impulse noise removal.

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Section: Numerical Experimentsmentioning

confidence: 99%

Effectiveness of Genetic Multistep Search in Unsupervised Design of Morphological Filters for Noise Removal

Hanada

Muneyasu

Asano

2010

IPSJ Online Transactions

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“…Secondly, we set the value of final σ at 0.5 to accomplish the aforementioned objective. Following the work done on (1+1)-EA in [7] we take up a threshold-based selection procedure for IWO-ID-SS. Here the offspring vector substitutes its parent vector in the new generation if its fitness is less than the fitness of the parent (in case of minimization problems) by a threshold margin .…”

Section: The Proposed Schemementioning

confidence: 99%

IWO with Increased Deviation and Stochastic Selection (IWO-ID-SS) for global optimization of noisy fitness functions

Suresh

Kundu

Ghosh

et al. 2009

2009 World Congress on Nature &Amp; Biologically Inspired Computing (NaBIC)

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“…A design can be specified by defining ranges of values for the design variables, e.g. "1:1:10" denotes integers from 1 to 10, whereas "1:10" denotes real numbers from the interval [1,10], or by specifying a set of values, e.g. "{1, 5, 10}".…”

Section: Experimental Designsmentioning

confidence: 99%

“…Threshold rejection has been proposed as a selection method for evolutionary algorithms, that accepts new candidates if their function values are significantly better than the values of the other candidates [10]. "Significant" is equivalent to "by at least a margin of τ ".…”

Section: Threshold Selection Algorithmsmentioning

confidence: 99%

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Evolution Strategies and Threshold Selection

Bartz-Beielstein

2005

Hybrid Metaheuristics

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Abstract. A hybrid approach that combines the (1 + 1)-ES and threshold selection methods is developed. The framework of the new experimentalism is used to perform a detailed statistical analysis of the effects that are caused by this hybridization. Experimental results on the sphere function indicate that hybridization worsens the performance of the evolution strategy, because evolution strategies are well-scaled hillclimbers: the additional threshold disturbs the self-adaptation process of the evolution strategy. Theory predicts that the hybrid approach might be advantageous in the presence of noise. This effect could be observedhowever, a proper fine tuning of the algorithm's parameters appears to be advantageous.

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Thresholding-a selection operator for noisy ES

Cited by 60 publications

References 4 publications

Effectiveness of Genetic Multistep Search in Unsupervised Design of Morphological Filters for Noise Removal

Effectiveness of Genetic Multistep Search in Unsupervised Design of Morphological Filters for Noise Removal

IWO with Increased Deviation and Stochastic Selection (IWO-ID-SS) for global optimization of noisy fitness functions

Evolution Strategies and Threshold Selection

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