Remember Me
Or use your Academic/Social account:


Or use your Academic/Social account:


You have just completed your registration at OpenAire.

Before you can login to the site, you will need to activate your account. An e-mail will be sent to you with the proper instructions.


Please note that this site is currently undergoing Beta testing.
Any new content you create is not guaranteed to be present to the final version of the site upon release.

Thank you for your patience,
OpenAire Dev Team.

Close This Message


Verify Password:
Verify E-mail:
*All Fields Are Required.
Please Verify You Are Human:
fbtwitterlinkedinvimeoflicker grey 14rssslideshare1
Leroux, Claris; Otero, Fernando E.B.; Johnson, Colin G. (2014)
Languages: English
Types: Unknown
Subjects: Q335
There are many different genetic programming (GP) frameworks that can be used to implement algorithms to solve a particular optimization problem. In order to use a framework, users need to become familiar with a large numbers of source code before actually implementing the algorithm, adding a learning overhead. In some cases, this can prevent users from trying out different frameworks. This paper discusses the implementation of a code generator in the EpochX framework to facilitate the implementation of GP algorithms. The code generator is based on the GP defini- tion language (GPDL), which is a framework-independent language that can be used to specify GP problems.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • [1] J. R. Koza. Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, 1992.
    • [2] G. Kronberger, M. Kommenda, S. Wagner, and H. Dobler. Gpdl: A framework-independent problem definition language for grammar-guided genetic programming. In GECCO'13 Companion, pages 1333-1340. ACM, 2013.
    • [3] D. J. Montana. Strongly typed genetic programming. Evolutionary Computation, 3(2):199-230, 1995.
    • [4] H. Mo¨ssenbo¨ck. A generator for fast compiler front-ends. In Report 127, Dept. Informatik, 28 pages. ETH Zu¨rich, 1990.
    • [5] M. O'Neill and C. Ryan. Grammatical evolution. IEEE Transactions on Evolutionary Computation, 5(4):349-358, Aug. 2001.
    • [6] F. Otero, T. Castle, and C. G. Johnson. Epochx: Genetic programming in java with statistics and event monitoring. In GECCO'12 Companion, Philadelphia, PA, USA, July 2012.
    • [7] L. Vaseux, F. Otero, T. Castle, and C. G. Johnson. Event-based graphical monitoring in the epochx genetic programming framework. In GECCO'13 Companion, Amsterdam, The Netherlands, July 2013.
    • [8] S. Wagner. Heuristic Optimization Software Systems - Modeling of Heuristic Optimization Algorithms in the HeuristicLab Software Environment. PhD thesis, Institute for Formal Models and Verification, Johannes Kepler University Linz, Austria, 2009.
    • [9] P. Whigham. Grammatically-based genetic programming. In Proceedings of the Workshop on Genetic Programming: From Theory to Real-World Applications, pages 33-41, 1995.
  • No related research data.
  • Discovered through pilot similarity algorithms. Send us your feedback.

Share - Bookmark

Download from

Cite this article