中文摘要：

由集成，合作社与快退火来临(革命算法(FAEA ) ，一个所谓的合作快退火 coevolutionary 算法(CFACA ) 为解决高度维的问题的目的在这篇论文被介绍。在在 CFACA 的搜索空间的分区以后，各更小的然后被分开的 FAEA 寻找。Thefitness 功能被联合每 FAEA 发现的亚答案评估。它证明 CFACA 在函数优化的域超过 FAEA，特别以集中率。当前的算法也被用于蛋白质主题抽取的一个真实优化问题。并且令人满意的结果与精确性被获得了完成 67.0% 的预言，它与在 PROSITE 数据库的结果一致。

英文摘要：

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.