Genetic Algorithm for Silhouette Matching

Genetic Algorithms (GAs) have been applied to matching problem. However, traditional GAs do not perform well in matching problem because there can be many locally similar parts. This paper presents a new genetic algorithm for silhouette matching. New concepts of partially matched genestrings in the initial population, the extending operator and the order adjustment algorithm are proposed. Each gene-string in the initial population only has three matched points while other points are unmatched. During the evolution, each gene-string will have more matched points due to the applications of the crossover and extending operators. The extending operator determines a potential match for an unmatched point near a matched point by searching the local space. After the application of the crossover and extending operators, the adjustment algorithm enforces each gene-string to be an ordered list by removing some matched points, if necessary. Our experiments show that the new matching algorithm based on GA performs better than traditional GA-based algorithms.