计算机科学
进化算法
多目标优化
选择(遗传算法)
数学优化
遗传算法
进化计算
最优化问题
算法
适应度函数
水准点(测量)
人工智能
帕累托原理
机器学习
粒子群优化
进化规划
作者
Ye Tian,Shichen Peng,Tobias Rodemann,Xingyi Zhang,Yaochu Jin
出处
期刊:IEEE Symposium Series on Computational Intelligence
日期:2019-12-01
卷期号:: 3225-3232
被引量:5
标识
DOI:10.1109/ssci44817.2019.9003018
摘要
In the last two decades, many evolutionary algorithms have shown promising performance in solving a variety of multi-objective optimization problems (MOPs). Since there does not exist an evolutionary algorithm having the best performance on all the MOPs, it is unreasonable to use a single evolutionary algorithm to tackle all the MOPs. Since many real-world MOPs are computationally expensive, selecting the best evolutionary algorithm from multiple candidates via empirical comparisons is also impractical. To address the above issues, this paper proposes an automated algorithm selection method for choosing the most suitable evolutionary algorithm for a given MOP. The proposed method establishes a predictor based on the performance of a set of candidate evolutionary algorithms on multiple benchmark MOPs, where the inputs of the predictor are the explicit and implicit features of an MOP, and the output is the index of the evolutionary algorithm having the best performance on the MOP. Experimental results indicate that the evolutionary algorithm suggested by the proposed method is highly competitive among all the candidate evolutionary algorithms, demonstrating the practical value of the proposed method for engineers to select an evolutionary algorithm for their applications.
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