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An artificial immune system with partially specified antibodies
, Article 9th Annual Genetic and Evolutionary Computation Conference, GECCO 2007, London, 7 July 2007 through 11 July 2007 ; Pages 57-62 , 2007 ; 9781595936974 (ISBN) ; Bagheri ShourakiS, S ; Jalali Heravi, M ; Jafari Jashmi, B ; Sharif University of Technology
2007
Abstract
Artificial Immune System algorithms use antibodies which fully specify the solution of an optimization, learning, or pattern recognition problem. By being restricted to fully specified antibodies, an AIS algorithm can not make use of schemata or classes of partial solutions. This paper presents a symbiotic artificial immune system (SymbAIS) algorithm which is an extension of CLONALG algorithm. It uses partially specified antibodies and gradually builds up building blocks of suitable sub-antibodies. The algorithm is compared with CLONALG on multimodal function optimization and combinatorial optimization problems and it is shown that it can solve problems that CLONALG is unable to solve....
Data mining with a simulated annealing based fuzzy classification system
, Article Pattern Recognition ; Volume 41, Issue 5 , 2008 , Pages 1824-1833 ; 00313203 (ISSN) ; Habibi, J ; Saniee Abadeh, M ; Saadi, H ; Sharif University of Technology
Elsevier Ltd
2008
Abstract
In this paper, the use of simulated annealing (SA) metaheuristic for constructing a fuzzy classification system is presented. In several previous investigations, the capability of fuzzy systems to solve different kinds of problems has been demonstrated. Simulated annealing based fuzzy classification system (SAFCS), hybridizes the learning capability of SA metaheuristic with the approximate reasoning method of fuzzy systems. The objective of this paper is to illustrate the ability of SA to develop an accurate fuzzy classifier. The use of SA in classification is an attempt to effectively explore and exploit the large search space usually associated with classification problems, and find the...
Pattern analysis by active learning method classifier
, Article Journal of Intelligent and Fuzzy Systems ; Vol. 26, issue. 1 , 2014 , p. 49-62 ; Shouraki, S. B ; Afrakoti, I. E. P ; Sharif University of Technology
Abstract
Active Learning Method (ALM) is a powerful fuzzy soft computing tool, developed originally in order to promote an engineering realization of human brain. This algorithm, as a macro-level brain imitation, has been inspired by some behavioral specifications of human brain and active learning ability. ALM is an adaptive recursive fuzzy learning algorithm, in which a complex Multi Input, Multi Output system can be represented as a fuzzy combination of several Single-Input, Single-Output systems. SISO systems as associative layer of algorithm capture partial spatial knowledge of sample data space, and enable a granular knowledge resolution tuning mechanism through the learning process. The...