An improved adaptive genetic algorithm and its application to image segmentation

Genetic Algorithm (GA) is derived from the mechanics of genetic adaptation in biological systems, which can search the global space of certain application effectively. The proposed algorithm introduces three parameters, fit(max), fit(min), and fit(ave), to measure how close the individuals are, so as to improve the Adaptive Genetic Algorithm (AGA) proposed by M. Sriniras. At the same time, the elitist strategy is employed to protect the best individual of each generation, and Remainder Stochastic Sampling with Replacement (RSSR) is employed in the proposed Improved Adaptive Genetic Algorithm (IAGA) to improve the basic reproduction operator. The proposed IAGA is applied to image segmentation. The experimental results exhibit satisfactory segmentation and demonstrate the learning capabilities of it. By determining p(c) and p(m) of the whole generation adaptively, it strikes a balance between the two incompatible goals: "sustain the global convergence capacity" and "converge rapidly to global optimum".