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http://dx.doi.org/10.5302/J.ICROS.2008.14.11.1110

Genetic Operators Based on Tree Structure in Genetic Programming  

Seo, Ki-Sung (서경대학교 전자공학과)
Pang, Cheul-Hyuk (서경대학교 전자공학과)
Publication Information
Journal of Institute of Control, Robotics and Systems / v.14, no.11, 2008 , pp. 1110-1116 More about this Journal
Abstract
In this paper, we suggest GP operators based on tree structure considering tree distributions in structure space and structural difficulties. The main idea of the proposed genetic operators is to place generated offspring into the specific region which nodes and depths are balanced and most of solutions exist. To enable that, the proposed operators are designed to utilize region information where parents belong and node/depth rates of selected subtree. To demonstrate the effectiveness of our proposed approach, experiments of binomial-3 regression, multiplexer and even parity problem are executed. The experiments results show that the proposed operators based on tree structure is superior to the results of standard GP for all three test problems in both success rate and number of evaluations.
Keywords
distribution of tree structures; genetic operators; genetic programming;
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  • Reference
1 J. M. Daida and A. M. Hilss, 'What Makes a Problem GP Hard? Validating a Hypothesis of Structural Causes,' in Proceeding of the Genetic and Evolutaionary Computation Conference (GECCO2003), LNCS 2724, pp.1665-1677, Chicago, IL, USA, July 2003
2 S. Luke, 'Issues in Scaling Genetic Programming: Breeding Strategies, Tree Generation, and Code Bloat,' PhD of University of Maryland, 2000
3 D. Zongker and B. Punch, lil-gp User's Manual, Michigan State University, July 1995
4 J. M. Daida and A. M. Hilss, 'Identifying Structural Mechanisms in Standard Genetic Programming,' in Proceeding of the Genetic and Evolutaionary Computation Conference (GECCO2003), LNCS 2724, pp.1639-1651, Chicago, IL, USA, July 2003
5 J. M. Daida, J. A. Polito, S. A. Stanhope, R. R. Berttam and J. C. Khoo, 'What Makes a Problem GP Hard? Analysis of a Turably Difficult Problem in Genetic Programming,' in proceedings of the Genetic Programming and Evolvable Machine, ISSN 1389-2576, Volume 2, Issue 2, pp.165-191, Hingham, MA, USA, June 2001
6 N. F. McPhee, A. Jarvis and E. F. Crane, 'On the Strength of Size Limits in Linear Genetic Programming,' in Proceeding of the Genetic and Evolutaionary Computation Conference (GECCO2004), LNCS 3103, pp.593-604, Seattle, WA, USA, June 2004
7 T. Ito, H. Iba and S. Sato, 'Depth dependent Crossover for Genetic Programming,' in Proceeding of the IEEE, pp.775-780, Anchorage, AK, USA, May 1998
8 H. Majeed and C. Ryan, 'On the Constructiveness of Context Avare Crossover,' in proceeding of the Genetic and Evolutionary Computation Conference (GECCO-07), pp.1659-1666, London, England, United Kingdom, July 2007
9 S. Silva and E. Costa, 'Resource Limited Genetic Programming : The Dynamic Approach,' in Proceeding of the Genetic and Evolutaionary Computation Conference (GECCO'05), pp.1673-1680, Washington, DC, USA, June 2005
10 J. R. Koza, F. H. Bennett, D. Andre and M. A. Keane, III, Darwinian Invention and Problem Solving, Morgan Kaufmann Publishers, USA, 1999
11 J. R. Koza, Genetic Programming : On the Programming of Computers by Natural Selection, MIT Press, Cambridge, MA, USA, 1992
12 Nguyen, X. Hoai, B. McKay and D. Essam, 'Representation and structural Difficulty in Genetic Programming,' Evolutionary computation, IEEE Transactions on Volume 10, Issue 2, pp.157-166, April 2006
13 R. Poli and J. Page, 'Solving High Order Boolean Parity Problems with Smooth Uniform Crossover, Sub Machine Code GP and Demes,' Genetic Programming and Evoluvable Machines, Volume 1, Issue 1/2, pp.37-56, April 2000   DOI
14 J. Page, R. Poli and W. B. Langdon, 'Smooth Uniform Crossover with Smooth Point Mutation in Genetic Programming : A Preliminary Study,' EuroGP'99, LNCS 1598, pp.39-48, Goteborg, Sweden, May 1999