Genetic algorithms: concepts and applications [in engineering design]

Man, K.F.; Tang, Wallace K. S.; Kwong, S.

doi:10.1109/41.538609

Cited by 1,101 publications

(567 citation statements)

References 35 publications

Supporting

Mentioning

507

Contrasting

Unclassified

Order By: Relevance

“…Table 1 Maximum power comparison between genetic algorithm and junction layers proposed in Ref. [3] Layer material (layer thickness) InGaP (0.50 mm) GaAs (1.00 mm) InGaNAs (1.54 mm) Ge (304 mm) …”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

The design and optimization of advanced multijunction solar cells using the Silvaco ATLAS software package

Michael

Bates

2005

Solar Energy Materials and Solar Cells

View full text Add to dashboard Cite

Section: Resultsmentioning

confidence: 99%

“…A method for the incorporation of a genetic search algorithm [3] to direct the ATLAS simulations have been met with early success. Fig.…”

Section: The Ingap/gaas/inganas/ge Quad-junction Cellmentioning

confidence: 99%

The design and optimization of advanced multijunction solar cells using the Silvaco ATLAS software package

Michael

Bates

2005

Solar Energy Materials and Solar Cells

View full text Add to dashboard Cite

“…The probabilistic voting approach illustrated above is very similar to natural selection which is one step of the procedure of genetic algorithms (Man et al 1996), i.e. the probability of a class being selected is very similar to the fitness of an individual involved in natural selection.…”

Section: Probabilistic Votingmentioning

confidence: 99%

Granular computing-based approach for classification towards reduction of bias in ensemble learning

Liu

Cocea

2016

Granul. Comput.

View full text Add to dashboard Cite

Machine learning has become a powerful approach in practical applications, such as decision making, sentiment analysis and ontology engineering. To improve the overall performance in machine learning tasks, ensemble learning has become increasingly popular by combining different learning algorithms or models. Popular approaches of ensemble learning include Bagging and Boosting, which involve voting towards the final classification. The voting in both Bagging and Boosting could result in incorrect classification due to the bias in the way voting takes place. To reduce the bias in voting, this paper proposes a probabilistic approach of voting in the context of granular computing towards improvement of overall accuracy of classification. An experimental study is reported to validate the proposed approach of voting using 15 data sets from the UCI repository. The results show that probabilistic voting is effective in increasing the accuracy through reduction of the bias in voting. This paper contributes to the theoretical and empirical analysis of causes of bias in voting, towards advancing ensemble learning approaches through the use of probabilistic voting.

show abstract

“…Basic genetic operations such as selection, crossover, and mutation [37][38][39] are involved in construction of the proposed GTI-TRSC. To construct a GTI-TRSC with high classification accuracy, n + 3 parameters-w 1 , w 2 , .…”

Section: Genetic-algorithm-based Learning Algorithmmentioning

confidence: 99%

Incorporating Grey Total Influence into Tolerance Rough Sets for Classification Problems

Chiu

2018

Applied Sciences

View full text Add to dashboard Cite

Tolerance-rough-set-based classifiers (TRSCs) are known to operate effectively on real-valued attributes for classification problems. This involves creating a tolerance relation that is defined by a distance function to estimate proximity between any pair of patterns. To improve the classification performance of the TRSC, distance may not be an appropriate means of estimating similarity. As certain relations hold among the patterns, it is interesting to consider similarity from the perspective of these relations. Thus, this study uses grey relational analysis to identify direct influences by generating a total influence matrix to verify the interdependence among patterns. In particular, to maintain the balance between a direct and a total influence matrix, an aggregated influence matrix is proposed to form the basis for the proposed grey-total-influence-based tolerance rough set (GTI-TRS) for pattern classification. A real-valued genetic algorithm is designed to generate the grey tolerance class of a pattern to yield high classification accuracy. The results of experiments showed that the classification accuracy obtained by the proposed method was comparable to those obtained by other rough-set-based methods.

show abstract

Genetic algorithms: concepts and applications [in engineering design]

Cited by 1,101 publications

References 35 publications

The design and optimization of advanced multijunction solar cells using the Silvaco ATLAS software package

The design and optimization of advanced multijunction solar cells using the Silvaco ATLAS software package

Granular computing-based approach for classification towards reduction of bias in ensemble learning

Incorporating Grey Total Influence into Tolerance Rough Sets for Classification Problems

Contact Info

Product

Resources

About