Does Kaizen Programming need a physic-informed mechanism to improve the search?

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{Ferreira:2023:LA-CCI,

• author = "Jimena Ferreira and Ana Ines Torres and Martin Pedemonte",
• booktitle = "2023 IEEE Latin American Conference on Computational Intelligence (LA-CCI)",
• title = "Does Kaizen Programming need a physic-informed mechanism to improve the search?",
• year = "2023",
• abstract = "In recent years, the study of physics-informed machine learning has increased. Works that use information about the shape or some characteristic of the expected function, have been used with genetic programming and neural networks. In those studies, it was found that including information about the expected model makes the resulting models better.Motivated by these studies, the goal of this work is the evaluation of the inclusion of information about the shape of the function in Kaizen Programming using a penalty function. In order to answer if the inclusion of this information in the search results in better models. In order to answer that we worked with 13 benchmark functions. The functions have between 2 and 9 input variables, and all have different types of shapes.We found that there is no significant difference in the performance of the models obtained using plain Kazan Programming and the shape-constrained approach.",
• keywords = "genetic algorithms, genetic programming, Shape, Input variables, Neural networks, ANN, Machine learning, Continuous improvement, Kaizen Programming, Evolutionary Computation, Physic-informed machine learning, Physic-informed symbolic regression",
• DOI = "doi:10.1109/LA-CCI58595.2023.10409360",
• ISSN = "2769-7622",
• month = oct,
• notes = "Also known as \cite{10409360}",

}

Genetic Programming entries for Jimena Ferreira Ana Ines Torres Martin Pedemonte

Citations