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Block building programming for symbolic regression
Chen C; Luo ZT(罗长童); Jiang ZL(姜宗林)
Source PublicationNEUROCOMPUTING
2018-01-31
Volume275Pages:1973-1980
ISSN0925-2312
Abstract

Symbolic regression that aims to detect underlying data-driven models has become increasingly important for industrial data analysis. For most existing algorithms such as genetic programming (GP), the convergence speed might be too slow for large-scale problems with a large number of variables. This situation may become even worse with increasing problem size. The aforementioned difficulty makes symbolic regression limited in practical applications. Fortunately, in many engineering problems, the independent variables in target models are separable or partially separable. This feature inspires us to develop a new approach, block building programming (BBP). BBP divides the original target function into several blocks, and further into factors. The factors are then modeled by an optimization engine (e.g. GP). Under such circumstances, BBP can make large reductions to the search space. The partition of separability is based on a special method, block and factor detection. Two different optimization engines are applied to test the performance of BBP on a set of symbolic regression problems. Numerical results show that BBP has a good capability of structure and coefficient optimization with high computational efficiency. (C) 2017 Elsevier B.V. All rights reserved.

KeywordSymbolic Regression Separable Function Block Building Programming Genetic Programming
DOI10.1016/j.neucom.2017.10.047
Indexed BySCI ; EI
Language英语
WOS IDWOS:000418370200184
WOS KeywordEvolution
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence
Funding OrganizationNational Natural Science Foundation of China(11532014)
Classification二类/q1
Ranking1
Citation statistics
Document Type期刊论文
Identifierhttp://dspace.imech.ac.cn/handle/311007/72232
Collection高温气体动力学国家重点实验室
Recommended Citation
GB/T 7714
Chen C,Luo ZT,Jiang ZL. Block building programming for symbolic regression[J]. NEUROCOMPUTING,2018,275:1973-1980.
APA Chen C,罗长童,&姜宗林.(2018).Block building programming for symbolic regression.NEUROCOMPUTING,275,1973-1980.
MLA Chen C,et al."Block building programming for symbolic regression".NEUROCOMPUTING 275(2018):1973-1980.
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