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We combine K-Nearest Neighbors (KNN) with genetic algorithm (GA) forphotometric redshift estimation of quasars, short for GeneticKNN, which is aweighted KNN approach supported by GA. This approach has two improvementscompared to KNN: one is the feature weighted by GA; another is that thepredicted redshift is not the redshift average of K neighbors but the weightedaverage of median and mean of redshifts for K neighbors, i.e. $p\timesz_{median} + (1-p)\times z_{mean}$. Based on the SDSS and SDSS-WISE quasarsamples, we explore the performance of GeneticKNN for photometric redshiftestimation, comparing with the other six traditional machine learning methods,i.e. Least absolute shrinkage and selection operator (LASSO), support vectorregression (SVR), Multi Layer Perceptrons (MLP), XGBoost, KNN and randomforest. KNN and random forest show their superiority. Considering the easyimplementation of KNN, we make improvement on KNN as GeneticKNN and applyGeneticKNN on photometric redshift estimation of quasars. Finally theperformance of GeneticKNN is better than that of LASSO, SVR, MLP, XGBoost, KNNand random forest for all cases. Moreover the accuracy is better with theadditional WISE magnitudes for the same method.

GeneticKNN: A Weighted KNN Approach Supported by Genetic Algorithm for Photometric Redshift Estimation of Quasars