A genetic algorithm-based edge detection technique

In this paper we present a genetic algorithm-based cost minimization technique for edge detection. Edge detection is formulated as a process of choosing a minimum cost edge configuration. The edge configurations are viewed as two-dimensional chromosomes with fitness values inversely proportional to their costs. The design of the crossover and the mutation operators is described. The knowledge-augmented mutation operator which exploits knowledge of the local edge structure is shown to result in rapid convergence. The incorporation of meta-level operators and strategies in the context of edge detection are discussed and are shown to improve the convergence rate.