Improving the prediction of material properties of concrete using Kaizen Programming with Simulated Annealing

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@Article{deMelo:2017:Neurocomputing,

• author = "Vinicius Veloso {de Melo} and Wolfgang Banzhaf",
• title = "Improving the prediction of material properties of concrete using Kaizen Programming with Simulated Annealing",
• journal = "Neurocomputing",
• year = "2017",
• volume = "246",
• pages = "25--44",
• month = "12 " # jul,
• note = "Brazilian Conference on Intelligent Systems 2015",
• keywords = "genetic algorithms, genetic programming, Automatic feature engineering, Kaizen Programming, Linear regression, High-performance concrete",
• ISSN = "0925-2312",
• URL = "https://www.cs.mun.ca/~banzhaf/papers/Neurocomputing2017.pdf",
• URL = "http://www.sciencedirect.com/science/article/pii/S092523121730231X",
• DOI = "doi:10.1016/j.neucom.2016.12.077",
• 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.",
• notes = "also known as \cite{VELOSODEMELO201725}",

}

Genetic Programming entries for Vinicius Veloso de Melo Wolfgang Banzhaf

Citations