- 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}",
