By integrating the concept of cooperative approach, an extension of the fast annealing coevolutionary algorithm is presented in this paper. It outperformed the original algorithm in the domain of function optimization, especially in terms of convergence rate. It was also applied to a real optimization problem, protein motif extraction. And a satisfactory result has been obtained with the accuracy of prediction achieving 67.0%, which is in agreement with the result in the PROSITE database.
Chao CHEN Yuan Xin TIAN Xiao Yong ZOU Pei Xiang CAI Jin Yuan MO
By integrating the cooperative approach with the fast annealing coevolutionary algorithm (FAEA), a so-called cooperative fast annealing coevolutionary algorithm (CFACA) is presented in this paper for the purpose of solving high-dimensional problems. After the partition of the search space in CFACA, each smaller one is then searched by a separate FAEA. The fitness function is evaluated by combining sub-solutions found by each of the FAEAs. It demonstrates that the CFACA outperforms the FAEA in the domain of function optimization, especially in terms of convergence rate. The current algorithm is also applied to a real optimization problem of protein motif extraction. And a satisfactory result has been obtained with the accuracy of prediction achieving 67.0%, which is in agreement with the result in the PROSITE database.
CHEN Chao TIAN YuanXin ZOU XiaoYong CAI PeiXiang MO JinYuan
