A quick convergent genetic algorithm for pattern alignment
Yo‐Ping Huang, Yueh-Tsun Chang · 2005
In this paper we present a modified genetic algorithm to overcome the slow convergent problem existed in traditional genetic algorithms. We introduce a new elite competition schema, which dominates the mutation and crossover operations, to expedite the evolution. On the benefits of the rapid convergence, our proposed algorithm is very suited to solve the optimization problems in many application domains. Moreover, to verify the effectiveness of the proposed model, we use the algorithm to solve the problems of polynomial fitting and gene sequence alignment. The experimental results demonstrate that our proposed algorithm is more efficient than traditional algorithms.