A Surrogate Model Based Genetic Algorithm for Complex Problem Solving. (September 2020)

• Record Type: Journal Article
• Authors: Pei, Ying; Gao, Hao; Han, Xiaosong
• Is Part Of: Journal of physics. Volume 1646(2020)
• Publication Date: 2020-09
• Subjects: Physics -- Congresses; 530.5
• DOI: 10.1088/1742-6596/1646/1/012153
• Languages: English
• ISSNs: 1742-6588

Abstract

Abstract: It is well known that when the fitness function is relatively complex, the optimization time cost of the genetic algorithm will be extremely huge. To address this issue, the surrogate model was employed to predict the fitness value of the optimization problem, to reduce the number of actual calculated fitness values. In this paper, BP neural network, the least square method and support vector machine were fused in the genetic algorithm to evaluate partial individuals' fitness. Sufficient benchmark numerical experiments were conducted, and the results proved that the strategy could reduce the calculating counts of fitness function on similar accuracy basis compared with simple genetic algorithm.