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  1. 1
    Automated path testing using the negative selection algorithm

    Automated path testing using the negative selection algorithm by Shayma Mustafa Mohi-Aldeen, Radziah Mohamad, Safaai Deris

    Published 2017
    “…Negative selection algorithm (NSA) has been used in this paper to generate test data for path testing automatically. …”
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  2. 2
    Model structure selection for a discrete-time non-linear system using genetic algorithm

    Model structure selection for a discrete-time non-linear system using genetic algorithm by Ahmad, Robiah, Jamaluddin , Hishamuddin, Hussain, Mohd. Azlan

    Published 2004
    “…In this paper, a methodology for model structure selection based on a genetic algorithm was developed and applied to non-linear discrete-time dynamic systems. …”
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  3. 3
    Model structure selection for a discrete-time non-linear system using a genetic algorithm

    Model structure selection for a discrete-time non-linear system using a genetic algorithm by Ahmad, R., Jamaluddin, H., Hussain, M. A.

    Published 2004
    “…In this paper, a methodology for model structure selection based on a genetic algorithm was developed and applied to non-linear discrete-time dynamic systems. …”
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  4. 4
    Model structure selection for a discrete-time non-linear system using a genetic algorithm

    Model structure selection for a discrete-time non-linear system using a genetic algorithm by Ahmad, R., Jamaluddin, H., Hussain, M. A.

    Published 2004
    “…In this paper, a methodology for model structure selection based on a genetic algorithm was developed and applied to non-linear discrete-time dynamic systems. …”
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  5. 5
    Minimization of Test Cases and Fault Detection Effectiveness Improvement through Modified Reduction with Selective Redundancy Algorithm

    Minimization of Test Cases and Fault Detection Effectiveness Improvement through Modified Reduction with Selective Redundancy Algorithm by Nikfal, Shima

    Published 2007
    “…To achieve such goal, this research modifies and improves the Reduction with Selective Redundancy (RSR) algorithm. In the modify algorithm, test cases would be selected according to the branch coverage if they covered different branch combination. …”
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  6. 6
    Enhancement of bearing defect diagnosis via genetic algorithm optimized feature selection

    Enhancement of bearing defect diagnosis via genetic algorithm optimized feature selection by Chia, Yee Shin

    Published 2015
    “…However this feature selection algorithm might be unstable due to the stochastic property of GA. …”
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  7. 7
    Model structure selection for a discrete-time non-linear system using a genetic algorithm

    Model structure selection for a discrete-time non-linear system using a genetic algorithm by Ahmad, R., Jamaluddin, H., Hussain, Mohd Azlan

    Published 2004
    “…In this paper, a methodology for model structure selection based on a genetic algorithm was developed and applied to non-linear discrete-time dynamic systems. …”
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  8. 8
    Improving Classification of Remotely Sensed Data Using Best Band Selection Index and Cluster Labelling Algorithms

    Improving Classification of Remotely Sensed Data Using Best Band Selection Index and Cluster Labelling Algorithms by Teoh, Chin Chuang

    Published 2005
    “…In addition, the best band selected for image classification is not necessarily the best for classification.A Best Band Selection Index (BBSI) algorithm was developed which is capable of selecting the best band combination for image visualization and supervised classification. …”
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  9. 9
    Clonal selection algorithm for the cryptanalysis of a simple substitution cipher

    Clonal selection algorithm for the cryptanalysis of a simple substitution cipher by Ahmad, Badrisham, Maarof, Mohd. Aizaini, Ibrahim, Subariah, Kutty Mammi, Hazinah, Mohamed Amin, Muhalim, Z'aba, Muhammad Reza

    Published 2006
    “…The development of Clonal Selection Algorithm (CSA) is inspired by elements from clonal selection theories. …”
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  10. 10
    The Development of an Algorithm to Determine Axial Capacity of Piles from SPT N-Values

    The Development of an Algorithm to Determine Axial Capacity of Piles from SPT N-Values by Ambrose, Jasmin

    Published 2000
    “…The developed IT was used for all cases of algorithm testing. …”
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  11. 11
    Sentiment classification for malay newspaper using clonal selection algorithm / Nur Fitri Nabila Mohamad Nasir

    Sentiment classification for malay newspaper using clonal selection algorithm / Nur Fitri Nabila Mohamad Nasir by Mohamad Nasir, Nur Fitri Nabila

    Published 2013
    “…In this study, sentiment classifier using clonal algorithm selection was developed to categorize sentiment in Malay newspaper (Berita Harian). …”
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  12. 12
    CTJ: Input-output based relation combinatorial testing strategy using Jaya algorithm

    CTJ: Input-output based relation combinatorial testing strategy using Jaya algorithm by Ng, Yeong Khang

    Published 2019
    “…IOR combinatorial testing only test for the important combinations that selected by tester. …”
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  13. 13
    A meta-heuristics based input variable selection technique for hybrid electrical energy demand prediction models

    A meta-heuristics based input variable selection technique for hybrid electrical energy demand prediction models by ul Islam, B., Baharudin, Z.

    Published 2017
    “…The focus of the paper is to propose a hybrid approach for the selection of the most influential input variables for the training and testing of neural network based hybrid models. …”
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  14. 14
    Development of automated neighborhood pattern sensitive faults syndrome generator for static random access memory

    Development of automated neighborhood pattern sensitive faults syndrome generator for static random access memory by Rusli, Julie Roslita

    Published 2011
    “…This tool can be used to ease the process of developing a new March test algorithm for NPSF.…”
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  15. 15
    Development of a Tuneable Test Problem Generator for Assembly Sequence Planning and Assembly Line Balancing

    Development of a Tuneable Test Problem Generator for Assembly Sequence Planning and Assembly Line Balancing by M. F. F., Ab Rashid, Hutabarat, Windo, Tiwari, Ashutosh

    Published 2012
    “…However, there is a scarcity in works that focus on developing problems to test these algorithms. In optimisation algorithm development, testing algorithms by a broad range of test problems is crucial to identify their strengths and weaknesses. …”
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  16. 16
    Development of artificial neural network models for predicting lipid profile using smartMF electrical parameters / Ahmad Zulkhairi Zulkefli

    Development of artificial neural network models for predicting lipid profile using smartMF electrical parameters / Ahmad Zulkhairi Zulkefli by Ahmad Zulkhairi , Zulkefli

    Published 2021
    “…For TG, the LM algorithm with a testing accuracy of 76.7%, sensitivity of 28.6% and specificity of 91.3% was selected. …”
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  17. 17
    SURE-Autometrics algorithm for model selection in multiple equations

    SURE-Autometrics algorithm for model selection in multiple equations by Norhayati, Yusof

    Published 2016
    “…Thus, this study aims to develop an algorithm for model selection in multiple equations focusing on seemingly unrelated regression equations (SURE) model. …”
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  18. 18
    Network reconfiguration and control for loss reduction using genetic algorithm

    Network reconfiguration and control for loss reduction using genetic algorithm by Jawad, Mohamed Hassan Izzaldeen

    Published 2010
    “…Whereas the results obtained from both selection methods are similar to each other. Tests results show that Genetic Algorithm is a suitable algorithm as it is an optimization technique with, high accuracy, and it avoids local minimum by searching in several regions to arrive to the global optimum solution. …”
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  19. 19
    Weed recognition based on erosion and dilation segmentation algorithm

    Weed recognition based on erosion and dilation segmentation algorithm by S., Sulaiman, M.H., Siddiqi, I., Ahmad

    Published 2009
    “…The developed algorithm has been tested on these two types of weeds in the lab, which gives a very reliable performance. …”
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  20. 20
    Semi-supervised learning for feature selection and classification of data / Ganesh Krishnasamy

    Semi-supervised learning for feature selection and classification of data / Ganesh Krishnasamy by Ganesh , Krishnasamy

    Published 2019
    “…The proposed algorithm is compared with the state-of-the-art feature selection algorithms using three different datasets. …”
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