Controlling The Problem Of Bloating Using Stepwise Crossover And Double Mutation Technique

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@Article{Bhardwaj:2011:ACIJ,

• author = "Arpit Bhardwaj and Aditi Sakalle and Harshita Chouhan and Harshit Bhardwaj",
• title = "Controlling The Problem Of Bloating Using Stepwise Crossover And Double Mutation Technique",
• year = "2011",
• journal = "Advanced Computing : an International Journal",
• volume = "2",
• number = "6",
• pages = "59--68",
• month = nov,
• publisher = "Academy \& Industry Research Collaboration Center (AIRCC)",
• keywords = "genetic algorithms, genetic programming, bloat, stepwise crossover, double mutation, elitism, fitness, Java, Oracle 10g",
• ISSN = "2229726X",
• URL = "http://airccse.org/journal/acij/papers/1111acij06.pdf",
• broken = "http://www.doaj.org/doaj?func=openurl\&genre=article\&issn=2229726X\&date=2011\&volume=2\&issue=6\&spage=59",
• DOI = "doi:10.5121/acij.2011.2606",
• size = "10 pages",
• abstract = "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 naturalised phenomenon characterised 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.",
• notes = "IRIS, WBC, BUPA, WINE, ABALONE, SPOKEN ARABIC DIGIT, HILL-VALLEY",
• bibsource = "OAI-PMH server at www.doaj.org",
• oai = "oai:doaj-articles:4e07bf6e3c343d42b02de6aed48a4d17",

}

Genetic Programming entries for Arpit Bhardwaj Aditi Sakalle Harshita Chouhan Harshit Bhardwaj

Citations