Multigene Genetic Programming Model for Temperature Optimization to Improve Lettuce Quality

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@InProceedings{Magsumbol:2021:HNICEM,

• author = "Jo-Ann V. Magsumbol and Maria Gemel B. Palconit and Lovelyn C. Garcia and Marife A. Rosales and Argel A. Bandala and Elmer P. Dadios",
• title = "Multigene Genetic Programming Model for Temperature Optimization to Improve Lettuce Quality",
• booktitle = "2021 IEEE 13th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM)",
• year = "2021",
• month = "28-30 " # nov,
• address = "Manila, Philippines",
• keywords = "genetic algorithms, genetic programming",
• isbn13 = "978-1-6654-0168-5",
• DOI = "doi:10.1109/HNICEM54116.2021.9731974",
• abstract = "This paper presents a Multigene Genetic Programming (MGGP) approach in optimizing the temperature of romaine lettuce inside an artificially controlled environment (ACE). In this research, MGGP is used to find the prediction model that will lead to the optimum temperature for growing lettuce crop. The system used a 1000 population using tournament selection with 40 generations. A mutation probability of 0.14 was applied to validate if it is at global optima. When the iterations reached the termination criteria, the system stopped, resulting in the best temperature model for growing lettuce crop. Training and testing of predictions were done. The model developed in this study can be used for the control system of the temperature setting inside the ACE which can provide optimal condition.",
• notes = "Also known as \cite{9731974}",

}

Genetic Programming entries for Jo-Ann V Magsumbol Maria Gemel B Palconit Lovelyn C Garcia Marife A Rosales Argel A Bandala Elmer Jose P Dadios

Citations