Modified Genetic Algorithm Parameters to Improve Online Character Recognition

Online character recognition is characterized with feature extraction and classification parameters that make recognition accuracy non-trivial task. Failure of existing optimization techniques to yield an acceptable solution to solve poor feature selection and slow convergence time provokes the idea for some stochastic algorithms. In this paper, a feature reduction technique that apply the power of genetic algorithm was modified using fitness function and genetic operators to minimize the aforementioned drawbacks. Two classifiers (C1 and C2) were then formulated from the integration of modified genetic algorithm (MGA) into an existing Modified Optical Backpropagation (MOBP) learning algorithm. The performance of C2 on generation gaps was further evaluated using convergence time and recognition accuracy. The research evaluation showed that C2 assumed average convergence times of 130.30, 211.69, 199.23 and 243.00 milliseconds with generation gaps of 0.1, 0.3, 0.5 and 0.7. This implies that generation gap variation had a positive effect on the network performance. Further evaluation showed that C2 assumed average recognition accuracies at 0.7 is 98.1% and 99.4% at Ggap 0.1 respectively.