@inproceedings{2ded80698c174a058da414ecbb7be1fd,
title = "Adaptive genetic algorithm for reliable training population in plant breeding genomic selection",
abstract = "Many algorithms are developed to model Genomic Estimated Breeding Value (GEBV). Modeling GEBV evolves a huge size of genotype in both terms of the dimension (columns) and the instances (rows). Good combinations of features help in predicting which phenotype is being represented. Preparing a good training population sample is assumed to be a convenient solution to deal with such complex genotype data. In this research, an Adaptive Genetic Algorithm (AGA) is proposed. The adaptive characteristic of AGA by adjusting probabilities in crossover and mutation is expected to converge into the global optimum without getting trapped in local optima. The proposed method using AGA to optimize the feature selection and shrinkage mechanism is looked forward to provide a reliable model to be reused in other similar datasets.",
keywords = "adaptive genetic algorithm, genomic selection, plant breeding, training population",
author = "Purbarani, {Sumarsih C.} and Ito Wasito and Ilham Kusuma",
note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 8th International Conference on Advanced Computer Science and Information Systems, ICACSIS 2016 ; Conference date: 15-10-2016 Through 16-10-2016",
year = "2017",
month = mar,
day = "6",
doi = "10.1109/ICACSIS.2016.7872803",
language = "English",
series = "2016 International Conference on Advanced Computer Science and Information Systems, ICACSIS 2016",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "556--563",
booktitle = "2016 International Conference on Advanced Computer Science and Information Systems, ICACSIS 2016",
address = "United States",
}