Multiclass classifier designing by Modified Crossover and Point Mutation technique using genetic programming

A Multiclass classifier is an approach for designing classifiers for an m-class (m>;=2) problem using genetic programming (GP). In this paper we proposed two methods named Modified Crossover Method and a Point Mutation method. In Point 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 Modified crossover together with Point Mutation improves the performance of the classifier.