Solving Classification Problems Using Infix Form Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.8120

@InProceedings{oltean:2003:AIDA,

• author = "Mihai Oltean and Crina Grosan",
• title = "Solving Classification Problems Using Infix Form Genetic Programming",
• booktitle = "Advances in Intelligent Data Analysis V",
• year = "2003",
• editor = "Michael R. Berthold and Hans-Joachim Lenz and Elizabeth Bradley and Rudolf Kruse and Christian Borgelt",
• volume = "2810",
• series = "Lecture Notes in Computer Science",
• pages = "242--253",
• address = "Berlin, Germany",
• month = aug # " 28-30",
• publisher = "Springer",
• keywords = "genetic algorithms, genetic programming",
• isbn13 = "978-3-540-40813-0",
• URL = "http://link.springer.com/chapter/10.1007/978-3-540-45231-7_23",
• URL = "https://rdcu.be/cT7kp",
• DOI = "doi:10.1007/978-3-540-45231-7_23",
• size = "12 pages",
• abstract = "A new evolutionary technique, called Infix Form Genetic Programming (IFGP) is proposed. The IFGP individuals are strings encoding complex mathematical expressions. The IFGP technique is used for solving several classification problems. All test problems are taken from PROBEN1 and contain real world data. IFGP is compared to Linear Genetic Programming (LGP) and Artificial Neural Networks (ANNs). Numerical experiments show that IFGP is able to solve the considered test problems with the same (and sometimes even better) classification error than that obtained by LGP and ANNs.",

}

Genetic Programming entries for Mihai Oltean Crina Grosan

Citations