初始化
进化算法
过度拟合
多目标优化
比例(比率)
计算机科学
数学优化
进化计算
水准点(测量)
测试套件
帕累托原理
遗传算法
算法
数学
人工智能
机器学习
人工神经网络
测试用例
物理
量子力学
回归分析
程序设计语言
地理
大地测量学
作者
Ye Tian,Xingyi Zhang,Chao Wang,Yaochu Jin
标识
DOI:10.1109/tevc.2019.2918140
摘要
In the last two decades, a variety of different types of multi-objective optimization problems (MOPs) have been extensively investigated in the evolutionary computation community.However, most existing evolutionary algorithms encounter difficulties in dealing with MOPs whose Pareto optimal solutions are sparse (i.e., most decision variables of the optimal solutions are zero), especially when the number of decision variables is large.Such large-scale sparse MOPs exist in a wide range of applications, for example, feature selection that aims to find a small subset of features from a large number of candidate features, or structure optimization of neural networks whose connections are sparse to alleviate overfitting.This paper proposes an evolutionary algorithm for solving large-scale sparse MOPs.The proposed algorithm suggests a new population initialization strategy and genetic operators by taking the sparse nature of the Pareto optimal solutions into consideration, to ensure the sparsity of the generated solutions.Moreover, this paper also designs a test suite to assess the performance of the proposed algorithm for large-scale sparse MOPs.Experimental results on the proposed test suite and four application examples demonstrate the superiority of the proposed algorithm over seven existing algorithms in solving large-scale sparse MOPs.
科研通智能强力驱动
Strongly Powered by AbleSci AI