publication . Thesis

Evolving High-Level Imperative Program Trees with Genetic Programming

Castle, Tom;
Open Access English
  • Country: United Kingdom
Abstract
Genetic Programming (GP) is a technique which uses an evolutionary metaphor to automatically generate computer programs. Although GP proclaims to evolve computer programs, historically it has been used to produce code which more closely resembles mathematical formulae than the well structured programs that modern programmers aim to produce. The objective of this thesis is to explore the use of GP in generating high-level imperative programs and to present some novel techniques to progress this aim.\ud \ud A novel set of extensions to Montana’s Strongly Typed Genetic Programming system are presented that provide a mechanism for constraining the structure of progr...
Subjects
free text keywords: QA76
86 references, page 1 of 6

[21] Jonathan Byrne, James McDermott, Michael O'Neill, and Anthony Brabazon. An analysis of the behaviour of mutation in grammatical evolution. In Anna I. Esparcia-Alcazar et al., editor, Proceedings of the 13th European Conference on Genetic Programming (EuroGP 2010), volume 6021 of LNCS, Istanbul, April 2010. Springer.

[22] Tom Castle, Lawrence Beadle, and Fernando E. B. Otero. Epochx: genetic programming software for research. http://www.epochx.org, 2007.

[23] Tom Castle and Colin G. Johnson. Positional effect of crossover and mutation in grammatical evolution. In Anna I. Esparcia-Alcazar et al., editor, Proceedings of the 13th European Conference on Genetic Programming (EuroGP 2010), volume 6021 of LNCS, Istanbul, April 2010. Springer.

[24] Phil T. Cattani and Colin G. Johnson. ME-CGP: Multi expression cartesian genetic programming. In Proceedings of the IEEE Congress on Evolutionary Computation (CEC 2010), Barcelona, July 2010. IEEE Press.

[25] Darren M. Chitty. A data parallel approach to genetic programming using programmable graphics hardware. In Dirk Thierens et al., editor, Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2007), volume 2, pages 1566-1573, London, July 2007. ACM Press. [OpenAIRE]

[26] Fuey Sian Chong and William B. Langdon. Java based distributed genetic programming on the internet. In Wolfgang Banzhaf et al., editor, Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 1999), pages 163-166, Orlando, July 1999.

[27] Steffen Christensen and Franz Oppacher. An analysis of Koza's computational effort statistic for genetic programming. In James A. Foster et al., editor, Proceedings of the 5th European Conference on Genetic Programming (EuroGP 2002), volume 2278 of LNCS, pages 182-191, Kinsale, April 2002. Springer.

[28] Vic Ciesielski and Xiang Li. Analysis of genetic programming runs. In R I Mckay and Sung-Bae Cho, editors, Proceedings of The Second Asian-Pacific Workshop on Genetic Programming, Cairns, December 2004.

[29] Vic Ciesielski and Xiang Li. Experiments with explicit for-loops in genetic programming. In Proceedings of the 2004 IEEE Congress on Evolutionary Computation (CEC 2004), pages 494-501, Portland, June 2004. IEEE Press. [OpenAIRE]

[30] Chris Clack and Tina Yu. Performance enhanced genetic programming. In Peter J. Angeline et al., editor, Proceedings of the Sixth Conference on Evolutionary Programming, volume 1213 of LNCS, Indianapolis, 1997. Springer. [OpenAIRE]

[31] Robert Cleary and Michael O'Neill. An attribute grammar decoder for the 01 multiconstrained knapsack problem. In Gu¨nther R. Raidl and Jens Gottlieb, editors, Proceedings of the 5th European Conference on Evolutionary Computation in Combinatorial Optimization (EvoCOP 2005), volume 3448 of LNCS, pages 34-45, Lausanne, March 2005. Springer.

[32] Nichael L. Cramer. A representation for the adaptive generation of simple sequential programs. In John J. Grefenstette, editor, Proceedings of the International Conference on Genetic Algorithms and the Applications, pages 183-187, Pittsburgh, July 1985.

[33] Ellery F. Crane and Nicholas F. McPhee. The effects of size and depth limits on tree based genetic programming. In Tina Yu, Rick L. Riolo, and Bill Worzel, editors, Genetic Programming Theory and Practice III, volume 9 of Genetic Programming, chapter 15, pages 223-240. Springer, May 2005.

[34] Ronald L. Crepeau. Genetic evolution of machine language software. In Justinian P. Rosca, editor, Proceedings of the Workshop on Genetic Programming: From Theory to Real-World Applications, pages 121-134, Tahoe City, July 1995.

[35] Bill Curtis, Sylvia B. Sheppard, Phil Milliman, M. A. Borst, and Tom Love. Measuring the psychological complexity of software maintenance tasks with the halstead and mccabe metrics. IEEE Transactions on Software Engineering, 5(2):96-104, March 1979.

86 references, page 1 of 6
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