NDLI: Automatic programming using genetic programming

Content Provider	IEEE Xplore Digital Library
Author	Igwe, K. Pillay, N.
Copyright Year	2013
Description	Author affiliation: Sch. of Math., Stat. & Comput. Sci., Univ. of KwaZulu-Natal, Natal, South Africa (Igwe, K.; Pillay, N.)
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 modularization. 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.
Starting Page	337
Ending Page	342
File Size	705544
Page Count	6
File Format	PDF
ISBN	9781479932306
DOI	10.1109/WICT.2013.7113158
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-12-15
Publisher Place	Vietnam
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Modularization Runtime Automatic programming Incremental learning Genetic programming Evolutionary computation Programming Genetics IP networks
Content Type	Text
Resource Type	Article

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