Multiclass Classifier Designed Using Stepwise Crossover and Special Mutation Technique

A Multiclass classifier is an approach for designing classifiers for a m-class (m>=2) problem using genetic programming (GP). In this paper we proposed three methods named Triple Tournament Method, Special Mutation Method and a Step Wise Crossover method. In Special Mutation technique we are generating the two child from single parent and selecting the one child on the basis of fitness and also applying the elitism on the child so that the mutation operation does not reduce the fitness of the individual and in Stepwise Crossover we select the two child for the next generation on the basis of size, depth and fitness along with elitism on each step from the six child which is generated during crossover. To demonstrate our approach we have designed a Multiclass Classifier using GP by taking few benchmark datasets. The results obtained show that by applying Stepwise crossover together with Special Mutation improves the performance of the classifier. In Triple Tournament Method, we select the two individual for the crossover operation on the basis of size, depth and fitness KEYWORD Elitism,Triple Tournament, Special Mutation, Stepwise Crossover.