Automatic bandwidth selection of fuzzy membership functions
Information Sciences: an International Journal
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Artificial Immune Systems: A New Computational Intelligence Paradigm
Artificial Immune Systems: A New Computational Intelligence Paradigm
Generating fuzzy membership function with self-organizing feature map
Pattern Recognition Letters
Improved binary PSO for feature selection using gene expression data
Computational Biology and Chemistry
Automatically constructing grade membership functions of fuzzy rules for students' evaluation
Expert Systems with Applications: An International Journal
An improved approach to find membership functions and multiple minimum supports in fuzzy data mining
Expert Systems with Applications: An International Journal
Dynamical membership functions: an approach for adaptive fuzzy modelling
Fuzzy Sets and Systems
H∞ estimation for fuzzy membership function optimization
International Journal of Approximate Reasoning
Handling constraints in global optimization using an artificial immune system
ICARIS'05 Proceedings of the 4th international conference on Artificial Immune Systems
Learning and optimization using the clonal selection principle
IEEE Transactions on Evolutionary Computation
Hi-index | 12.05 |
A clonal selection algorithm (CLONALG) inspires from clonal selection principle used to explain the basic features of an adaptive immune response to an antigenic stimulus. It takes place in various scientific applications and it can be also used to determine the membership functions in a fuzzy system. The aim of the study is to adjust the shape of membership functions and a novice aspect of the study is to determine the membership functions. Proposed method has been implemented using a developed CLONALG program for a multiple input-output (MI-O) fuzzy system. In this study, GA and binary particle swarm optimization (BPSO) are used for implementing the proposed method as well and they are compared. It has been shown that using clonal selection algorithm is advantageous for finding optimum values of fuzzy membership functions