Evolutionary computing in manufacturing industry: an overview of recent applications

Oduguwa, Victor; Tiwari, Ashutosh; Roy, Rajkumar

doi:10.1016/j.asoc.2004.08.003

Cited by 129 publications

(56 citation statements)

References 55 publications

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“…A recent survey on the use of evolutionary computing to solving complex real world problems is also presented in [32]. A Genetic programming approach for epileptic pattern recognition in electroecephalographic signals has been reported in [33].…”

Section: Discussionmentioning

confidence: 99%

Evolving a Bayesian classifier for ECG-based age classification in medical applications

Wiggins

Saad

Litt

et al. 2008

Applied Soft Computing

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Objective-To classify patients by age based upon information extracted from their electrocardiograms (ECGs). To develop and compare the performance of Bayesian classifiers.Methods and Material-We present a methodology for classifying patients according to statistical features extracted from their ECG signals using a genetically evolved Bayesian network classifier. Continuous signal feature variables are converted to a discrete symbolic form by thresholding, to lower the dimensionality of the signal. This simplifies calculation of conditional probability tables for the classifier, and makes the tables smaller. Two methods of network discovery from data were developed and compared: the first using a greedy hill-climb search and the second employed evolutionary computing using a genetic algorithm (GA).Results and Conclusions-The evolved Bayesian network performed better (86.25% AUC) than both the one developed using the greedy algorithm (65% AUC) and the naïve Bayesian classifier (84.75% AUC). The methodology for evolving the Bayesian classifier can be used to evolve Bayesian networks in general thereby identifying the dependencies among the variables of interest. Those dependencies are assumed to be non-existent by naïve Bayesian classifiers. Such a classifier can then be used for medical applications for diagnosis and prediction purposes.

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Section: Discussionmentioning

confidence: 99%

Evolving a Bayesian classifier for ECG-based age classification in medical applications

Wiggins

Saad

Litt

et al. 2008

Applied Soft Computing

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“…Very general attempts to bridge the gap between theory and practice by exploring characteristics of real world problems and by surveying recent evolutionary computation applications for solving real problems in the manufacturing industry were described in [9,10]. The survey outlines the current status and trends of EC applications in manufacturing industry.…”

Section: Introductionmentioning

confidence: 99%

Genetic Based Approach to Predicting the Elongation of Drawn Alloy

Gusel¹,

Rudolf²,

Brezočnik³

2015

Int. j. simul. model.

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This paper describes a genetic based approach for the modelling of elongation in cold drawn copper alloy. Genetic programming is one of the most general genetic based methods and was used in our research. It is an automated evolutionary computation method for creating a working computer programme from a problem's high-level statement. Genetic programming does this by breeding a population of computer programmes genetically using the principles of Darwinian's natural selection and biologically inspired operations. In our research, material was formed by drawing using different process parameters and then determining elongation of the specimens. On the basis of a training data set, various different genetic models for the elongation distribution were developed during simulated evolution. The accuracies of the best models were proved by a testing data set and comparison between the genetic and regression models was carried out.

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“…According to a review of the literature, genetic (evolutionary) algorithms are one of the most popular optimization methods. Genetic algorithms are widely used to solve complex (nonlinear and multidimensional) problems in various fields of scientific research [5][6][7][8][9][10], because they eliminate many of the errors that are encountered in classical optimization methods. The main advantages of genetic algorithms include the multi-directional search for a solution space, resistance to local extrema, the exclusive use of objective functions in calculations (without the need for additional information, such as derivatives of the objective function), and their applicability for solving multi-modal and multiple-criterion problems [4].…”

Section: Introductionmentioning

confidence: 99%

Optimization of the Separation Parameters and Indicators of Separation Efficiency of Buckwheat Seeds

2017

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Abstract:The separation parameters and the indicators of separation efficiency for buckwheat seeds and impurities that are difficult to separate were optimized with the use of self-designed software based on genetic algorithms. The results of the calculations differed significantly from the suboptimal values determined in previous studies. The optimal values of the indicator of separation efficiency were higher; whereas the values of the indicator of buckwheat seed loss were significantly lower. The optimal working parameters for a seed separator in order to promote separation efficiency were determined.

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Evolutionary computing in manufacturing industry: an overview of recent applications

Cited by 129 publications

References 55 publications

Evolving a Bayesian classifier for ECG-based age classification in medical applications

Evolving a Bayesian classifier for ECG-based age classification in medical applications

Genetic Based Approach to Predicting the Elongation of Drawn Alloy

Optimization of the Separation Parameters and Indicators of Separation Efficiency of Buckwheat Seeds

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