Improving the prediction of material properties of concrete using Kaizen Programming with Simulated Annealing
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- @Article{deMelo:2017:Neurocomputing,
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author = "Vinicius Veloso {de Melo} and Wolfgang Banzhaf",
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title = "Improving the prediction of material properties of
concrete using Kaizen Programming with Simulated
Annealing",
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journal = "Neurocomputing",
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year = "2017",
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volume = "246",
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pages = "25--44",
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month = "12 " # jul,
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note = "Brazilian Conference on Intelligent Systems 2015",
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keywords = "genetic algorithms, genetic programming, Automatic
feature engineering, Kaizen Programming, Linear
regression, High-performance concrete",
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ISSN = "0925-2312",
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URL = "https://www.cs.mun.ca/~banzhaf/papers/Neurocomputing2017.pdf",
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URL = "http://www.sciencedirect.com/science/article/pii/S092523121730231X",
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DOI = "doi:10.1016/j.neucom.2016.12.077",
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abstract = "Predicting the properties of materials like concrete
has been proven a difficult task given the complex
interactions among its components. Over the years,
researchers have used Statistics, Machine Learning, and
Evolutionary Computation to build models in an attempt
to accurately predict such properties. High-quality
models are often non-linear, justifying the study of
nonlinear regression tools. In this paper, we employ a
traditional multiple linear regression method by
ordinary least squares to solve the task. However, the
model is built upon non-linear features automatically
engineered by Kaizen Programming, a recently proposed
hybrid method. Experimental results show that Kaizen
Programming can find low-correlated features in an
acceptable computational time. Such features build
high-quality models with better predictive quality than
results reported in the literature.",
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notes = "also known as \cite{VELOSODEMELO201725}",
- }
Genetic Programming entries for
Vinicius Veloso de Melo
Wolfgang Banzhaf
Citations