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Title:
Block building programming for symbolic regression
Author: Chen C(陈辰); Luo ZT(罗长童); Jiang ZL(姜宗林)
Source: NEUROCOMPUTING
Issued Date: 2018-01-31
Volume: 275, Pages:1973-1980
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.

Keyword: Symbolic regression ; Separable function ; Block building programming ; Genetic programming
Language: 英语
Indexed Type: SCI ; EI
DOI: 10.1016/j.neucom.2017.10.047
DOC Type: Article
WOS Subject: Computer Science, Artificial Intelligence
WOS Subject Extended: Computer Science
WOS Keyword Plus: EVOLUTION
WOS ID: WOS:000418370200184
ISSN: 0925-2312
Funder: National Natural Science Foundation of China(11532014)
Classification: 二类/Q1
Ranking: 1
Citation statistics:
Content Type: 期刊论文
URI: http://dspace.imech.ac.cn/handle/311007/72232
Appears in Collections:高温气体动力学国家重点实验室_期刊论文

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Recommended Citation:
Chen C,Luo ZT,Jiang ZL. Block building programming for symbolic regression[J]. NEUROCOMPUTING,2018-01-31,275:1973-1980.
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