Block building programming for symbolic regression

NEUROCOMPUTING

卷:275

Article

Chen, Chen^1,2  Luo, Changtong¹  Jiang, Zonglin^1,2 

[1] Chinese Acad Sci, Inst Mech, State Key Lab High Temp Gas Dynam, Beijing 100190, Peoples R China

[2] Univ Chinese Acad Sci, Sch Engn Sci, Beijing 049, Peoples R China

关键词: Symbolic regression;    Separable function;    Block building programming;    Genetic programming;   

DOI  :  10.1016/j.neucom.2017.10.047

来源: Elsevier

【 摘 要 】

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.

【 授权许可】

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