期刊名称：Advanced Computing : an International Journal
印刷版ISSN：2229-726X
电子版ISSN：2229-6727
出版年度：2011
卷号：2
期号：6
DOI：10.5121/acij.2011.2606
出版社：Academy & Industry Research Collaboration Center (AIRCC)
摘要：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. The conception of “bloat” in Genetic Programming is a well naturalized phenomenon characterized by variable-length genomes gradually maturating in size during evolution. “In a very real sense, bloating makes genetic programming a race against time, to find the best solution possible before bloat puts an effective stop to the search.” In this paper we are proposing a Stepwise crossover and double mutation operation in order to reduce the bloat. In this especial crossover operation we are using local elitism replacement in combination with depth limit and size of the trees to reduce the problem of bloat substantially without compromising the performance. The use of local elitism in crossover and mutation increases the accuracy of the operation and also reduces the problem of bloat and further improves the performance. To shew our approach we have designed a Multiclass Classifier using GP by taking few benchmark datasets.
