Antibody Remainder Method Based Artificial Immune System for Mathematical Function Optimization

Yap, David F. W. and Koh, S. P. and Tiong, S. K. (2011) Antibody Remainder Method Based Artificial Immune System for Mathematical Function Optimization. In: 3rd International Conference on Machine Learning and Computing (ICMLC 2011) , 26-28 Feb 2011, Singapore.

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Abstract

Artificial immune system (AIS) is one of the natureinspired algorithm for solving optimization problem. In AIS, clonal selection algorithm (CSA) is able to improve global searching ability. However, the CSA convergence and accuracy can be improved further because the hypermutation in CSA itself cannot always guarantee a better solution. Alternatively,Genetic Algorithms (GAs) and Particle Swarm Optimization(PSO) have been used efficiently in solving complex optimization problems, but they have a tendency to converge prematurely. In this study, the CSA is modified using the best solution for each exposure (iteration) namely Single Best Remainder (SBR) CSA. In this study, the results show that the performance of the proposed algorithm (SBR-CSA) compares favourably with other algorithms while Half Best Insertion (HBI) CSA produced moderate results in most of the simulations.

Item Type:	Conference or Workshop Item (Paper)
Uncontrolled Keywords:	clone, hypermutation, antigen, affinity maturation, antibody.
Subjects:	T Technology > TA Engineering (General). Civil engineering (General)
Divisions:	Faculty of Electronics and Computer Engineering > Department of Telecommunication Engineering
Depositing User:	Dr David Yap
Date Deposited:	10 Jul 2012 02:07
Last Modified:	28 May 2015 02:38
URI:	http://eprints.utem.edu.my/id/eprint/3973
Statistic Details:	View Download Statistic

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