Probabilistic Adaptive Crossover Applied to Chilean Wine Classification

Duarte‐Mermoud, Manuel A.; Beltrán, Nicolás H.; Salah, S.A.

doi:10.1155/2013/734151

Cited by 3 publications

(1 citation statement)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Among many optimal algorithms [20][21][22][23][24][25][26][27][28][29][30][31][32][33], PSO algorithm has been the most famous global optimization algorithm which is based on swarm intelligence. And it is easy to be realized and already applied to many fields.…”

Section: Introductionmentioning

confidence: 99%

An Image Filter Based on Shearlet Transformation and Particle Swarm Optimization Algorithm

Hu¹,

Song

Xia³

et al. 2015

Mathematical Problems in Engineering

View full text Add to dashboard Cite

Digital image is always polluted by noise and made data postprocessing difficult. To remove noise and preserve detail of image as much as possible, this paper proposed image filter algorithm which combined the merits of Shearlet transformation and particle swarm optimization (PSO) algorithm. Firstly, we use classical Shearlet transform to decompose noised image into many subwavelets under multiscale and multiorientation. Secondly, we gave weighted factor to those subwavelets obtained. Then, using classical Shearlet inverse transform, we obtained a composite image which is composed of those weighted subwavelets. After that, we designed fast and rough evaluation method to evaluate noise level of the new image; by using this method as fitness, we adopted PSO to find the optimal weighted factor we added; after lots of iterations, by the optimal factors and Shearlet inverse transform, we got the best denoised image. Experimental results have shown that proposed algorithm eliminates noise effectively and yields good peak signal noise ratio (PSNR).

show abstract

Section: Introductionmentioning

confidence: 99%

An Image Filter Based on Shearlet Transformation and Particle Swarm Optimization Algorithm

Hu¹,

Song

Xia³

et al. 2015

Mathematical Problems in Engineering

View full text Add to dashboard Cite

show abstract

Improving Neuroevolution with Complementarity-Based Selection Operators

Maul

2016

Neural Process Lett

View full text Add to dashboard Cite

A Systematic Literature Review of Adaptive Parameter Control Methods for Evolutionary Algorithms

2016

View full text Add to dashboard Cite

Evolutionary algorithms (EAs) are robust stochastic optimisers that perform well over a wide range of problems. Their robustness, however, may be affected by several adjustable parameters, such as mutation rate, crossover rate, and population size. Algorithm parameters are usually problem-specific, and often have to be tuned not only to the problem but even the problem instance at hand to achieve ideal performance. In addition, research has shown that different parameter values may be optimal at different stages of the optimisation process. To address these issues, researchers have shifted their focus to adaptive parameter control, in which parameter values are adjusted during the optimisation process based on the performance of the algorithm. These methods redefine parameter values repeatedly based on implicit or explicit rules that decide how to make the best use of feedback from the optimisation algorithm. In this survey, we systematically investigate the state of the art in adaptive parameter control. The approaches are classified using a new conceptual model that subdivides the process of adapting parameter values into four steps that are present explicitly or implicitly in all existing approaches that tune parameters dynamically during the optimisation process. The analysis reveals the major focus areas of adaptive parameter control research as well as gaps and potential directions for further development in this area.

show abstract

Probabilistic Adaptive Crossover Applied to Chilean Wine Classification

Cited by 3 publications

References 42 publications

An Image Filter Based on Shearlet Transformation and Particle Swarm Optimization Algorithm

An Image Filter Based on Shearlet Transformation and Particle Swarm Optimization Algorithm

Improving Neuroevolution with Complementarity-Based Selection Operators

A Systematic Literature Review of Adaptive Parameter Control Methods for Evolutionary Algorithms

Contact Info

Product

Resources

About