Automatic Programming Using Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3949

@InProceedings{Igwe:2013:WICT,
  author =       "Kevin Igwe and Nelishia Pillay",
  title =        "Automatic Programming Using Genetic Programming",
  booktitle =    "Proceedings of the 2013 Third World Congress on
                 Information and Communication Technologies (WICT
                 2013)",
  year =         "2013",
  editor =       "Long Thanh Ngo and Ajith Abraham and Lam Thu Bui and 
                 Emilio Corchado and Choo Yun-Huoy and Kun Ma",
  pages =        "337--342",
  address =      "Hanoi, Vietnam",
  month =        "15-18 " # dec,
  publisher =    "IEEE",
  keywords =     "genetic algorithms, genetic programming, Automatic
                 programming, incremental learning, modularisation",
  isbn13 =       "978-1-4799-3230-6",
  URL =          "http://www.mirlabs.net/wict13/proceedings/html/paper91.xml",
  URL =          "http://www.mirlabs.net/wict13/proceedings/pdf/paper91.pdf",
  URL =          "http://www.titan.cs.unp.ac.za/~nelishiap/uploads/5.pdf",
  DOI =          "doi:10.1109/WICT.2013.7113158",
  size =         "6 pages",
  abstract =     "Genetic programming (GP) is an evolutionary algorithm
                 which explores a program space rather than a solution
                 space which is typical of other evolutionary algorithms
                 such as genetic algorithms. GP finds solutions to
                 problems by evolving a program, which when implemented
                 will produce a solution. This paper investigates the
                 use of genetic programming for automatic programming.
                 The paper focuses on the procedural/imperative
                 programming paradigm. More specifically the evolution
                 of programs using memory, conditional and iterative
                 programming constructs is investigated. An internal
                 representation language is defined in which to evolve
                 programs. The generational GP algorithm was implemented
                 using the grow method to create the initial population,
                 tournament selection to choose parents and
                 reproduction, crossover and mutation for regeneration
                 purposes. The paper also presents a form of incremental
                 learning which facilitates modularisation. The GP
                 approach to automatic programming was tested on ten
                 programming problems that are usually presented to
                 novice programmers in a first year procedural
                 programming course of an undergraduate degree in
                 Computer Science. The GP approach evolved solutions for
                 all ten problems, with incremental learning needed in
                 two instances to produce a solution.",
  notes =        "memory, if-then-else, while, blockn, combn.
                 Encapsulation. Factorial, square, swap, reverse, sum,
                 complex root, vowel, vat, salary. Netbeans JDK 1.7.2_25
                 IEEE Catalogue Number: CFP1368R-POD
                 http://www.mirlabs.net/wict13/proceedings/index.html

                 Also known as \cite{7113158}",
}

Genetic Programming entries for Kevin C Igwe Nelishia Pillay

Citations