中文摘要：

算法选择的目的是从众多可用优化算法中自动地选出最适用于当前问题的算法。针对算法选择问题提出了基于元学习推荐的优化算法自动选择框架。依据此框架,以多模式资源受限的项目调度问题为实证数据集,设计实现了遗传算法（GA）、粒子群算法（PSO）和模拟退火算法（SA）三种算法的自动选择过程。从项目调度问题数据库中随机选取了378个问题算例,提取其中的固有特征和统计特征作为元数据,并利用前馈型神经网络（FNN）算法训练获得用于预测的元模型对未见算例作出预测。实证结果表明两选一的算法预测准确率最高可超过95%,交叉验证准确率平均达到85%;三选一的算法预测准确率最高可达92%,交叉验证准确率平均超过80%。实证结果验证了所提算法选择框架是成功的,基于元学习思想的优化算法自动选择方法是可行的。

英文摘要：

The goal of algorithm selection is to automatically select the best suitable algorithm for current problem from a batch of available algorithms. For this purpose, an intelligent recommendation framework based on meta-learning approach was presented. The automatic selection procedure for Genetic Algorithm（GA）, Particle Swarm Optimization（PSO） and Simulated Annealing（SA） was designed according to this framework by using Multi-mode Resource-Constrained Project Scheduling Problem（MRCPSP） as the validation data set. Three hundred and seventy-eight instances of MRCPSP were randomly picked out from the Project Scheduling Problem Library（PSPLib）, and the inherent and statistic features of each instance were extracted and used as the metadata, then the prediction meta-model for new examples was obtained by using Feed-forward Neural Network（FNN） algorithm. The empirical results demonstrate that the hit rate reaches 95% at most, and the average hit rate is 85% when choosing one algorithm from two ones; the best hit rate reaches 92% and 80% respectively when choosing one algorithm from three ones. The proposed intelligent recommendation framework is successful and the automatic selection for optimization algorithms is feasible.