A hybrid genetic algorithm for classification
James D. Kelly, Lawrence D. Davis · 1991
In this paper we describe a method for hybridizing a genetic algorithm and a k nearest neighbors classification algorithm. We use the genetic algorithm and a training data set to learn real-valued weights associated with individual attributes in the data set. We use the k nearest neighbors algorithm to classify new data records based on their weighted distance from the members of the training set. We applied our hybrid algorithm to three test cases. Classification results obtained with the hybrid algorithm exceed the performance of the k nearest neighbors algorithm in all three cases. 1