Performance improvement in genetic programming using modified crossover and node mutation

Created by W.Langdon from gp-bibliography.bib Revision:1.8129

@InProceedings{Bhardwaj:2013:GECCOcomp,

• author = "Arpit Bhardwaj and Aruna Tiwari",
• title = "Performance improvement in genetic programming using modified crossover and node mutation",
• booktitle = "GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion",
• year = "2013",
• editor = "Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A. N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks",
• isbn13 = "978-1-4503-1964-5",
• keywords = "genetic algorithms, genetic programming",
• pages = "1721--1722",
• month = "6-10 " # jul,
• organisation = "SIGEVO",
• address = "Amsterdam, The Netherlands",
• DOI = "doi:10.1145/2464576.2480787",
• publisher = "ACM",
• publisher_address = "New York, NY, USA",
• 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. 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.",
• notes = "Also known as \cite{2480787} Distributed at GECCO-2013.",

}

Genetic Programming entries for Arpit Bhardwaj Aruna Tiwari

Citations