Ant colony optimization with selective evaluation for feature selection in character recognition

This paper analyzes the size characteristics of character recognition domain with the aim of developing a feature selection algorithm adequate for the domain. Based on the results, we further analyze the timing requirements of three popular feature selection algorithms, greedy algorithm, genetic algorithm, and ant colony optimization. For a rigorous timing analysis, we adopt the concept of atomic operation. We propose a novel scheme called selective evaluation to improve convergence of ACO. The scheme cut down the computational load by excluding the evaluation of unnecessary or less promising candidate solutions. The scheme is realizable in ACO due to the valuable information, pheromone trail which helps identify those solutions. Experimental results showed that the ACO with selective evaluation was promising both in timing requirement and recognition performance.