Block building programming for symbolic regression
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @Article{CHEN20181973,
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author = "Chen Chen and Changtong Luo and Zonglin Jiang",
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title = "Block building programming for symbolic regression",
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journal = "Neurocomputing",
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year = "2018",
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volume = "275",
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pages = "1973--1980",
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month = "31 " # jan,
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keywords = "genetic algorithms, genetic programming, Symbolic
regression, Separable function, Block building
programming",
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ISSN = "0925-2312",
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URL = "http://www.sciencedirect.com/science/article/pii/S0925231217316983",
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DOI = "doi:10.1016/j.neucom.2017.10.047",
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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 modelled 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.",
- }
Genetic Programming entries for
Chen Chen
Changtong Luo
Zonglin Jiang
Citations