Optimization of Neuro-Fuzzy System Using Genetic Algorithm for Chromosome Classification

M. Sarosa (1School of Electrical Engineering and Informatics, Institute of Technology Bandung, 2 Departement of Electrical Engineering, Polytechnic of Malang, Indonesia)
A. S. Ahmad (1School of Electrical Engineering and Informatics, Institute of Technology Bandung,)
B. Riyanto (1School of Electrical Engineering and Informatics, Institute of Technology Bandung,)
A. S. Noer (Division of Biochemistry, Institute of Technology Bandung, Indonesia)

Journal of ICT Research and Applications
Vol. 1 No. 1 (2007)

Neuro-fuzzy system has been shown to provide a good performance on chromosome classification but does not offer a simple method to obtain the accurate parameter values required to yield the best recognition rate. This paper presents a neuro-fuzzy system where its parameters can be automatically adjusted using genetic algorithms. The approach combines the advantages of fuzzy logic theory, neural networks, and genetic algorithms. The structure consists of a four layer feed-forward neural network that uses a GBell membership function as the output function. The proposed methodology has been applied and tested on banded chromosome classification from the Copenhagen Chromosome Database. Simulation result showed that the proposed neuro-fuzzy system optimized by genetic algorithms offers advantages in setting the parameter values, improves the recognition rate significantly and decreases the training/testing time which makes genetic neuro-fuzzy system suitable for chromosome classification.