Solving computational algorithm using CLONALNet technique based on artificial clonal selection
This paper discusses the approach of CLONALNet in determining the optimum fitness function and mean population by benchmarking it with CLONALG. By using this algorithm the steps to obtain the fitness function is optimized and processing time is reduced. CLONALNet is a hybrid or combination of both o...
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IEEE Computer Society
2023
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| author | Samy J.A.L.A. Krishnan P.S.A.L. Kiong T.S. |
| author2 | 54584226700 |
| author_facet | 54584226700 Samy J.A.L.A. Krishnan P.S.A.L. Kiong T.S. |
| author_sort | Samy J.A.L.A. |
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| collection | Institutional Repository |
| content_provider | Universiti Tenaga Nasional |
| content_source | UNITEN Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | This paper discusses the approach of CLONALNet in determining the optimum fitness function and mean population by benchmarking it with CLONALG. By using this algorithm the steps to obtain the fitness function is optimized and processing time is reduced. CLONALNet is a hybrid or combination of both opt-aiNet (Optimize Artificial Immune Network) and CLONALG. CLONALNet enforces an algorithm that is much more robust in evaluating the fitness for each antibody cells since it initiates boundaries so that the initialization process doesn't run off as of previous CLONALG algorithm but still maintains the immune network interaction as in aiNet. Also included is the combination of steps which includes cloning, affinity maturation and selection steps into one single function to find the best group of clones. In recent studies done, the maximum value will be the optimum solution. The optimum result as suggested in this paper is the minimum value for fitness function referring to global optimum result which is zero. � 2011 IEEE. |
| format | Conference paper |
| id | my.uniten.dspace-30491 |
| institution | Universiti Tenaga Nasional |
| publishDate | 2023 |
| publisher | IEEE Computer Society |
| record_format | dspace |
| spelling | my.uniten.dspace-304912023-12-29T15:48:27Z Solving computational algorithm using CLONALNet technique based on artificial clonal selection Samy J.A.L.A. Krishnan P.S.A.L. Kiong T.S. 54584226700 36053261400 15128307800 CLONALNet data acquisition Gaussian Multifunction mutation rate Clone cells Cloning Data acquisition Affinity maturation Artificial immune networks CLONALNet Computational algorithm Fitness functions Gaussians Multifunction Mutation rates Health This paper discusses the approach of CLONALNet in determining the optimum fitness function and mean population by benchmarking it with CLONALG. By using this algorithm the steps to obtain the fitness function is optimized and processing time is reduced. CLONALNet is a hybrid or combination of both opt-aiNet (Optimize Artificial Immune Network) and CLONALG. CLONALNet enforces an algorithm that is much more robust in evaluating the fitness for each antibody cells since it initiates boundaries so that the initialization process doesn't run off as of previous CLONALG algorithm but still maintains the immune network interaction as in aiNet. Also included is the combination of steps which includes cloning, affinity maturation and selection steps into one single function to find the best group of clones. In recent studies done, the maximum value will be the optimum solution. The optimum result as suggested in this paper is the minimum value for fitness function referring to global optimum result which is zero. � 2011 IEEE. Final 2023-12-29T07:48:26Z 2023-12-29T07:48:26Z 2011 Conference paper 10.1109/ICOS.2011.6079255 2-s2.0-83155178468 https://www.scopus.com/inward/record.uri?eid=2-s2.0-83155178468&doi=10.1109%2fICOS.2011.6079255&partnerID=40&md5=7d47b796576bcf758f846241bed4c243 https://irepository.uniten.edu.my/handle/123456789/30491 6079255 344 347 IEEE Computer Society Scopus |
| spellingShingle | CLONALNet data acquisition Gaussian Multifunction mutation rate Clone cells Cloning Data acquisition Affinity maturation Artificial immune networks CLONALNet Computational algorithm Fitness functions Gaussians Multifunction Mutation rates Health Samy J.A.L.A. Krishnan P.S.A.L. Kiong T.S. Solving computational algorithm using CLONALNet technique based on artificial clonal selection |
| title | Solving computational algorithm using CLONALNet technique based on artificial clonal selection |
| title_full | Solving computational algorithm using CLONALNet technique based on artificial clonal selection |
| title_fullStr | Solving computational algorithm using CLONALNet technique based on artificial clonal selection |
| title_full_unstemmed | Solving computational algorithm using CLONALNet technique based on artificial clonal selection |
| title_short | Solving computational algorithm using CLONALNet technique based on artificial clonal selection |
| title_sort | solving computational algorithm using clonalnet technique based on artificial clonal selection |
| topic | CLONALNet data acquisition Gaussian Multifunction mutation rate Clone cells Cloning Data acquisition Affinity maturation Artificial immune networks CLONALNet Computational algorithm Fitness functions Gaussians Multifunction Mutation rates Health |
| url_provider | http://dspace.uniten.edu.my/ |