Abstract
A fundamental issue in evolutionary learning is the definition of the solution representation language. We present the application of Object Oriented Genetic Programming to the task of coevolving general recursive sorting algorithms along with their primitive representation alphabet. We report the computational effort required to evolve target solutions and provide a comparison between crossover and mutation variation operators, and also undirected random search. We found that the induction of evolved method signatures (typed parameters and return type) can be realized through an evolutionary fitness-driven process. We also found that the evolutionary algorithm outperformed undirected random search, and that mutation performed better than crossover in this problem domain. The main result is that modular sorting algorithms can be evolved.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Koza, J.R.: Genetic Programming II: automatic discovery of reusable programs. MIT Press, Cambridge (1994)
Angeline, P.J., Pollack, J.: Evolutionary module acquisition. In: Proceedings of the Second Annual Conference on Evolutionary Programming (1993)
Rosca, J.P., Ballard, D.H.: Discovery of subroutines in genetic programming. In: Advances in Genetic Programming 2. MIT Press, Cambridge (1996)
Spector, L.: Simultaneous evolution of programs and their control structures. In: Advances in Genetic Programming 2. MIT Press, Cambridge (1996)
Yu, T., Clack, C.: Recursion, lambda abstractions and genetic programming. In: Genetic Programming 1998, Proceedings of the Third Annual Conference (1998)
Spector, L., Klein, J., Keijzer, M.: The push3 execution stack and the evolution of control. In: Proceedings of the 2005 conference on Genetic and evolutionary computation, GECCO ’05, New York, NY, USA, pp. 1689–1696 (2005)
Agapitos, A., Lucas, S.M.: Evolving efficient recursive sorting algorithms. In: Proceedings of the 2006 IEEE Congress on Evolutionary Computation, Vancouver, 6-21 July, pp. 9227–9234. IEEE Press, Los Alamitos (2006)
Kinnear Jr., K.E.: Generality and difficulty in genetic programming: Evolving a sort. In: Forrest, S. (ed.) Proceedings of the 5th International Conference on Genetic Algorithms, ICGA-93, University of Illinois at Urbana-Champaign, 17-21 July, pp. 287–294. Morgan Kaufmann, San Francisco (1993)
Kinnear Jr., K.E.: Evolving a sort: Lessons in genetic programming. In: Proceedings of the 1993 International Conference on Neural Networks, vol. 2, San Francisco, USA, 28 March-1 April 1993, pp. 881–888. IEEE Press, Los Alamitos (1993)
O’Reilly, U.-M., Oppacher, F.: An experimental perspective on genetic programming. In: Parallel Problem Solving from Nature 2 (1992)
Abbott, R., Guo, J., Parviz, B.: Guided genetic programming. In: The 2003 International Conference on Machine Learning; Models, Technologies and Applications (MLMTA’03), Las Vegas, 23–26 June. CSREA Press (2003)
Brave, S.: Evolving recursive programs for tree search. In: Advances in Genetic Programming 2, MIT Press, Cambridge (1996)
Teller, A.: Genetic programming, indexed memory, the halting problem, and other curiosities. In: Proceedings of the 7th annual Florida Artificial Intelligence Research Symposium, Pensacola, Florida, USA, May 1994, pp. 270–274. IEEE Press, Los Alamitos (1994)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer Berlin Heidelberg
About this paper
Cite this paper
Agapitos, A., Lucas, S.M. (2007). Evolving Modular Recursive Sorting Algorithms. In: Ebner, M., O’Neill, M., Ekárt, A., Vanneschi, L., Esparcia-Alcázar, A.I. (eds) Genetic Programming. EuroGP 2007. Lecture Notes in Computer Science, vol 4445. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71605-1_28
Download citation
DOI: https://doi.org/10.1007/978-3-540-71605-1_28
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-71602-0
Online ISBN: 978-3-540-71605-1
eBook Packages: Computer ScienceComputer Science (R0)