Prototype selection based on minimal consistent subset and genetic algorithms

This paper applies the genetic algorithms to identify the minimal ldquoconsistentrdquo prototype subset . This subset can be used as a prototype which correctly recognizes the entire original prototype set. This proposed genetic algorithm tries to find the minimal consistent subset to reduce recognition time in nearest neighbor classification. The main difference from other genetic algorithm (GA) approaches is the hybrid of minimal consistent set identification (MCSI) method and genetic algorithm. The MCSI method provides the local optimal number of prototype while the Genetic performs the global search. The proposed hybrid algorithm has been tested on several problems and compared with the results of MCSI and other GA approach.