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Appraisal of genetic algorithm and its application in 0-1 knapsack problem

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
A lot of uncertainties and complexities exist in real life problem. Unfortunately, the world approaches such intricate realistic life problems using traditional methods which has failed to offer robust solutions. In recent times, researchers look beyond classical techniques. There is a model shift from the use of classical techniques to the use of standardized intelligent biological systems or evolutionary biology. Genetic Algorithm (GA) has been recognized as a prospective technique capable of handling uncertainties and providing optimized solutions in diverse area, especially in homes, offices, stores and industrial operations. This research is focused on the appraisal of GA and its application in real life problem. The scenario considered is the application of GA in 0-1 knapsack problem. From the solution of the GA model, it was observed that there is no combination that would give the exact weight or capacity the 35 kg bag can carry but the possible range from the solution model is 34 kg and 36 kg. Since the weight of the bag is 35 kg, the feasible or near optimal solution weight of items the bag can carry would be 34 kg at benefit of 16. Additional load beyond 34 kg could lead to warping of the bag.
Rocznik
Strony
39--46
Opis fizyczny
Bibliogr. 29 poz., rys., tab.
Twórcy
  • Department of Mechanical Engineering, Federal University of Petroleum Resources, Effurun, P.M.B. 1221,Effurun, Delta State, Nigeria
  • Department of Mechanical Engineering, Federal University of Petroleum Resources, Effurun, P.M.B. 1221,Effurun, Delta State, Nigeria
  • Department of Mechanical Engineering, University of Lagos, Univ. Road, Akoka, Yaba, 101017 Lagos, Nigeria
  • Department of Mechanical Engineering, Federal University of Petroleum Resources, Effurun, P.M.B. 1221,Effurun, Delta State, Nigeria
  • Center for Asset Integrity Management, Department of Mechanical and Aeronautical Engineering, University of Pretoria, 83 Lynnwood Rd, Hartebeestpoort 362-Jr, 0083 Pretoria, South Africa
Bibliografia
  • 1. Cerrada M., Sánchez R.V., Cabrera D., Zurita G. and Li C. (2015), “Multi-Stage Feature Selection by Using Genetic Algorithms for Fault Diagnosis in Gearboxes Based on Vibration Signal”, Sensors 2015, 15, pp. 23903-23926
  • 2. Dimeo R., and Lee K. Y., (1995), “Boiler-Turbine Control System Design Using a Genetic Algorithm”, IEEE Transactions on Energy Conversion, Vol. 10, No. 4, pp. 752-759
  • 3. Gaspar P., Carbonell J., and Oliveira J.L. (2012) “Parameter Influence in Genetic Algorithm Optimization of Support Vector Machines”, 6th International Conference on PACBB, AISC 154, pp. 43-51
  • 4. Gent M., Cheng R. and Wang D. (1997), “Genetic Algorithms for Solving Shortest Path Problems” IEEE pp. 401-406
  • 5. Hajnayeb A., Gahsemloonia A., Hahdem S. E. and Moradi M. H. (2011), “Application and Comparison ofan ANN-Based Feature Selection and Genetic Algorithm in Gearbox fault diagnosis”, Expert Systems with Applications. 38, pp 10205-10209
  • 6. Hussain S. and Gabbar H. A. (2014), “Gearbox Fault Detection Using Real Coded Genetic Algorithm and Novel Shock Response Spectrum Features Extraction”, Journal of Nondestruct. Eval 33: pp.111-123
  • 7. Jack L. B and Nandi A. K. (1999), “Feature Selection for ANNs using Genetic Algorithms in Condition Monitoring”, ESANN'1999 proceedings - European Symposium on Artificial Neural Networks Bruges (Belgium), 21-23 April 1999, D-Facto public., ISBN 2-600049-9-X, pp. 313-318
  • 8. Jack L. B and Nandi A. K. (2000), “Genetic Algorithms for Feature Selection in Machine Condition Monitoring with Vibration Signals”, IEE Proc-Vis. Image Signal Process, Vol. 147, No 3, doi: 10.1049/ip-vis:20000325
  • 9. Jack L. B and Nandi A. K. (2002), “Fault Detection Using Support Vector Machines and Artificial Neural Networks Augmented by Genetic Algorithms”, Mechanical Systems and Signal Processing (2002) 16(2-3), pp. 373-390
  • 10. Kang M., Kim J., Choi B-K. and Kim J-M. (2015), “Envelope Analysis with a Genetic Algorithm-Based Adaptive filter Bank for Bearing Fault Detection”, Acoustical Society of America, 138 (1). https://doi.org/10.1121/1.4922767
  • 11. Kita H. and Sane Y. (2003), “Genetic Algorithms for Optimization of Uncertain Functions and Their Applications”, SICE Annual Conference in Fukui. pp.2744-2749
  • 12. Kuncheva L. I. and Jain L. C. (2000), “Designing Classifier Fusion Systems by Genetic Algorithms”, IEEE Transactions on Evolutionary Computation, Vol. 4, No. 4, pp. 327-336
  • 13. Liao P., Zhang X., and Li K. (2016), “Parameter Optimization for Support Vector Machine Based on Nested Genetic Algorithms”, Journal of Automation and Control Engineering Vol. 4, No. 1 pp. 78-83
  • 14. Loo C. K.,Liew W. S.,Seera M. and Lim E. (2015),“Probabilistic Ensemble Fuzzy ARTMAP Optimization using Hierarchical Parallel Genetic Algorithms”, Neural Comput&Applic 26: pp. 263-276
  • 15. Malhotra R., Singh N. & Singh Y. (2011), “Genetic Algorithms: Concepts, Design for Optimization of Process Controllers”, Computer and Information Science Vol. 4, No. 2; pp. 39-54
  • 16. Marmelstein, R. E. (1997), “Application of Genetic Algorithms to Data Mining”, MAICS-97 Proceedings. AAAI (www.aaai.org)
  • 17. Mininno E., Cupertino F. and Naso D. (2008), “Real-Valued Compact Genetic Algorithms for Embedded Microcontroller Optimization”,IEEE Transaction on Evolutionary Computation, VOL. 12, NO. 2, pp 203-219
  • 18. Omoregbee H. O. and Heyns P. S. (2018), “Fault Classification of Low Speed Bearings based on Support Vector Machine for Regression and Genetic Algorithms using Acoustic Emission”, Journal of Vibration Engineering & Technologies. https://doi.org/10.1007/s42417-019-00143-y
  • 19. Population-Based Approaches to Computer Intelligence. Hoboken: Wiley
  • 20. Rana K. P. S., Kumar V. and Nair S. S. (2016), “Efficient FIR Filter Designs using Constrained Genetic Algorithms based Optimization”, IEEE 2nd International Conference on Communication, Control and Intelligent Systems (CCIS). pp. 131-135.
  • 21. Raymer M. L., Punch W. F., Goodman E. D., Kuhn L. A., and Jain A. K. (2000), “Dimensionality Reduction Using Genetic Algorithms”, IEEE Transactions on Evolutionary Computation, Vol. 4, No. 2, pp. 164-171
  • 22. Samadzadegan F., Soleymani A. and Abbaspour R. A. (2010), “Evaluation of Genetic Algorithms for Tuning SVM Parameters in Multi-Class Problems”, 11th IEEE International Symposium on Computational Intelligence and Informatics, November, 2010. Budapest, Hungary. pp. 18-20
  • 23. Samanta B. (2004), “Gear Fault Detection Using Artificial Neural Networks and Support Vector Machines with Genetic Algorithms”, Mechanical Systems and Signal Processing 18 pp. 625-644
  • 24. Samanta B., Al-Balushi K. R. and Al_Araimi S. A. (2003), “Artificial Neural Networks and Support Vector machine with Genetic Algorithms for Bearing Fault Detection”, Journal on Applied Signal Processing. 16, pp 657-665
  • 25. Samanta B., Al-Balushi K. R. and Al_Araimi S. A. (2004), “Bearing Fault Detection Using Artificial Neural Networks and Genetic Algorithms”, Journal on Applied Signal Processing. 3, pp 366-377
  • 26. Samanta B., Al-Balushi K. R. and Al_Araimi S. A. (2006), “Artificial Neural Networks and Genetic Algorithm for Bearing Fault Detection” Soft Comput (2006) 10: pp 264-271
  • 27. Simon, D. (2013). Evolutionary Optimization Algorithms: Biologically-Inspired
  • 28. Suckley D., (1991), “Genetic algorithm in the design of FIR filters” IEE Proceedings-G, Vol. 138, No. 2 pp. 234-238.
  • 29. Tan F., Fu X., Zhang Y. and Bourgeois A. G. (2008), “A genetic algorithm-based method for feature subset selection”, Soft Comput, 12, pp 111-120
Uwagi
PL
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-46f7d58b-ca84-425d-88b9-83fc11825e9b
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