An Improved Adaptive Genetic Algorithm

The genetic algorithm (GA) is derived from the mechanics of genetic adaptation in biological systems, which can search the global space of certain applications effectively. The proposed algorithm introduces three parameters, i.e. fit max , fit min and fit ave to measure how close the individuals are, thus improving the adaptive genetic algorithm (AGA) proposed by M. Sriniras. Furthermore, the elitist strategy is employed to protect the best individual of each generation, and the remainder stochastic sampling with replacement (RSSR) is employed in the proposed IAGA to improve the basic reproduction operator. The proposed IAGA is applied to image segmentation. The experimental results exhibit a satisfactory segmentation and demonstrate its learning capabilities. By determining p\-c and p\-m of the whole generation adaptively, it strikes a balance between two incompatible goals: sustain the global convergence capability and converge rapidly to global optimum.