A Cost Minimization Approach to Edge Detection Using Simulated Annealing
IEEE Transactions on Pattern Analysis and Machine Intelligence
A novel genetic algorithm based on immunity
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
A new evolutionary system for evolving artificial neural networks
IEEE Transactions on Neural Networks
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An adaptive immune genetic algorithm (AIGA) based on cost minimization technique method for edge detection is proposed. The proposed AIGA recommends the use of adaptive probabilities of crossover, mutation and immune operation, and a geometric annealing schedule in immune operator to realize the twin goals of maintaining diversity in the population and sustaining the fast convergence rate in solving the complex problems such as edge detection. Furthermore, AIGA can effectively exploit some prior knowledge and information of the local edge structure in the edge image to make vaccines, which results in much better local search ability of AIGA than that of the canonical genetic algorithm. Experimental results on gray-scale images show the proposed algorithm perform well in terms of quality of the final edge image, rate of convergence and robustness to noise.