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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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Main Authors: Samy, J.A.L.A., Krishnan, P.S.A.L., Kiong, T.S.
Format: Conference Paper
Language:en_US
Published: 2017
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spelling my.uniten.dspace-59992018-02-20T02:56:00Z Solving computational algorithm using CLONALNet technique based on artificial clonal selection Samy, J.A.L.A. Krishnan, P.S.A.L. Kiong, T.S. 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. 2017-12-08T07:49:37Z 2017-12-08T07:49:37Z 2011 Conference Paper 10.1109/ICOS.2011.6079255 en_US In 2011 IEEE Conference on Open Systems, ICOS 2011 (pp. 350-353). [6079255]
institution Universiti Tenaga Nasional
building UNITEN Library
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continent Asia
country Malaysia
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content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
language en_US
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
author Samy, J.A.L.A.
Krishnan, P.S.A.L.
Kiong, T.S.
spellingShingle Samy, J.A.L.A.
Krishnan, P.S.A.L.
Kiong, T.S.
Solving computational algorithm using CLONALNet technique based on artificial clonal selection
author_facet Samy, J.A.L.A.
Krishnan, P.S.A.L.
Kiong, T.S.
author_sort Samy, J.A.L.A.
title 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_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_sort solving computational algorithm using clonalnet technique based on artificial clonal selection
publishDate 2017
_version_ 1644493818798538752
score 13.211869

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