A multilevel block building algorithm for fast modeling generalized separable systems
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- @Article{CHEN:2018:ESA,
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author = "Chen Chen and Changtong Luo and Zonglin Jiang",
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title = "A multilevel block building algorithm for fast
modeling generalized separable systems",
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journal = "Expert Systems with Applications",
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volume = "109",
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pages = "25--34",
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year = "2018",
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keywords = "genetic algorithms, genetic programming, Symbolic
regression, Generalized separability, Multilevel block
building",
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ISSN = "0957-4174",
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DOI = "doi:10.1016/j.eswa.2018.05.021",
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URL = "http://www.sciencedirect.com/science/article/pii/S0957417418303142",
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abstract = "Symbolic regression is an important application area
of genetic programming (GP), aimed at finding an
optimal mathematical model that can describe and
predict a given system based on observed input-response
data. However, GP convergence speed towards the target
model can be prohibitively slow for large-scale
problems containing many variables. With the
development of artificial intelligence, convergence
speed has become a bottleneck for practical
applications. In this paper, based on observations of
real-world engineering equations, generalized
separability is defined to handle repeated variables
that appear more than once in the target model. To
identify the structure of a function with a possible
generalized separability feature, a multilevel block
building (MBB) algorithm is proposed in which the
target model is decomposed into several blocks and then
into minimal blocks and factors. The minimal factors
are relatively easy to determine for most conventional
GP or other non-evolutionary algorithms. The efficiency
of the proposed MBB has been tested by comparing it
with Eureqa, a state-of-the-art symbolic regression
tool. Test results indicate MBB is more effective and
efficient; it can recover all investigated cases
quickly and reliably. MBB is thus a promising algorithm
for modeling engineering systems with separability
features",
- }
Genetic Programming entries for
Chen Chen
Changtong Luo
Zonglin Jiang
Citations