@inbook{eb25dadee56b4ebcbcc34d2af4f84f7e,
title = "A hybrid optimization method of Multi-Objective Genetic Algorithm (MOGA) and K-Nearest Neighbor (KNN) classifier for hydrological model calibration",
abstract = "The MOGA is used as automatic calibration method for a wide range of water and environmental simulation models.The task of estimating the entire Pareto set requires a large number of fitness evaluations in a standard MOGA optimization process. However, it's very time consuming to obtain a value of objective functions in many real engineering problems. We propose a unique hybrid method of MOGA and KNN classifier to reduce the number of actual fitness evaluations. The test results for multi-objective calibration show that the proposed method only requires about 30% of actual fitness evaluations of the MOGA.",
author = "Yang Liu and Khu, {Soon Thiam} and Dragon Savic",
year = "2004",
doi = "10.1007/978-3-540-28651-6_80",
language = "English",
isbn = "3540228810",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "546--551",
editor = "Yang, {Zheng Rong} and Richard Everson and Hujun Yin",
booktitle = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
}