Genetic matching pursuits based on diversity mutation
Yawen Li, YU Feng-qin · 2011
Genetic matching pursuit algorithm can improve the speed of finding the best atom, the convergence of the algorithm is reduced, because the mutation operator is easy to destroy the optimal individuals. In this paper, a new mutation operator named diversity mutation operator is proposed to improve genetic matching pursuit algorithm, which is moderated by the colony diversity. Higher the population diversity is, lower the probability of mutation is. When the population diversity is small, big mutation rate is needed to raise the population diversity. However, When the population diversity is big, small mutation rate is needed to avoid destroy optimal individuals. Simulation results show that the improved genetic matching algorithms is effective by the both in residual energy and searching time.