|Table of Contents|

Offline Data-Driven Evolutionary Algorithm Using Best Point and Uncertainty Point to Guide Model Selection(PDF)

《南京师大学报（自然科学版）》[ISSN:1001-4616/CN:32-1239/N]

Issue:
2022年03期
Page:
1-8
Research Field:
数学
Publishing date:

Info

Title:
Offline Data-Driven Evolutionary Algorithm Using Best Point and Uncertainty Point to Guide Model Selection
Author(s):
Bao Jianyang1; Lv Qiuyue1; Sun Yuehong1; 2
(1.School of Mathematical Sciences,Nanjing Normal University,Nanjing 210023,China)(2.Jiangsu Provincial Key Laboratory for Numerical Simulation of Large Scale Complex Systems,Nanjing 210023,China)
Keywords:
ensemble learning; evolutionary algorithm; offline data-driven optimization; surrogate model
PACS:
TP18
DOI:
10.3969/j.issn.1001-4616.2022.03.001
Abstract:
The off-line data-driven evolutionary optimization algorithm based on two-point model selection is mainly used to solve off-line optimization problems with high computational complexity. In the process of model establishment,several agent models are established,and then some agent models are selected by model selection strategy to form an integrated model. At the same time,the probability of model selection strategy is adopted to improve the generality of the algorithm and reduce the time complexity. Numerical experiments are carried out on common benchmark test functions,and the experimental results show that the new algorithm has more advantages than other advanced algorithms.

References:

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Last Update: 2022-09-15