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Performance improvement in genetic programming using modified crossover and node mutation

During the evolution of solutions using Genetic Programming (GP) there is generally an increase in average tree size without a corresponding increase in fitness'a phenomenon commonly referred to as bloat. Bloating increases time to find the best solution. Sometimes, best solution can never be obtained. In this paper we are proposing a modified crossover and point mutation operation in GP algorithm in order to reduce the problem of bloat. 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 modified node mutation reduces the problem of bloat substantially without compromising the performance.