Comparison of immune and genetic algorithms for parameter optimization of plate color recognition

To address the parameter optimization problem of plate color recognition, two approaches based on IA (immune algorithm) and GA (genetic algorithm) are proposed respectively. Theoretical comparison of IA and GA is first made. Then experimental comparison of the two algorithms is given by using them to perform the parameter optimization task for color recognition of license plates. For plate color recognition algorithm, color features are extracted in the HSV (hue, saturation, and value) color space and weighted fusion of the fuzzy maps on three components is utilized to perform color recognition. To improve the adaptability of recognition algorithm, weights of color feature components and thresholds of classification functions are optimized by immune and genetic algorithms respectively. Comparison experiments were conducted on three data sets. And the experimental results show that the immune-based approach achieves higher accuracy and smaller mean square deviation. From the theoretical and experimental comparisons, it is shown that many immune mechanisms, such as clonal explosion, immune supplementation, concentration adjustment, etc. can be used to solve the parameter optimization problem effectively and efficiently.