Adaptive recommendation model using meta-learning for population-based algorithms

作者：Chu, Xianghua; Cai, Fulin; Cui, Can; Hu, Mengqi; Li, Li; Qin, Quande^*

来源：Information Sciences, 2019, 476: 192-210.

DOI：10.1016/j.ins.2018.10.013

• Global optimization
• Algorithm selection
• Meta-learning
• Population-based algorithm
• Recommendation system

To efficiently solve complex optimization problems, numerous population-based meta heuristics and extensions have been developed. However, the performances of the algorithms vary depending on the problems. In this research, we propose an Adaptive Recommendation Model (ARM) using meta-learning to identify appropriate problem-dependent population-based algorithm. In ARM, the algorithms are adaptively selected by mapping the problem characteristics to the algorithm performance. Since the meta-features extracted and meta-learner adopted would significantly affect the system performance, 18 meta-features including statistical, geometrical and landscape features are extracted to characterize optimization problem spaces. Both instance-based and model-based learners are investigated. Two performance metrics, Spearman's rank correlation coefficient and success rate are used to evaluate the accuracy of optimizer's ranking prediction and the precision of the best optimizer recommendation. The proposed ARM is compared against population-based algorithms with distinct search capabilities such as PSO variants, non-PSO population-based optimizers, hyper-heuristics and ensemble methods. Benchmark functions and real-world problems with various properties are adopted in the experiments. Experimental results reveal the extendibility and effectiveness of ARM on the diverse tested problems in terms of solution accuracy, ranking and success rate.

• 出版日期2019-2
• 单位深圳大学