Abstract
Many forms of parsimony pressure are parametric, that is final fitness is a parametric model of the actual size and raw fitness values. The problem with parametric techniques is that they are hard to tune to prevent size from dominating fitness late in the evolutionary run, or to compensate for problem-dependent nonlinearities in the raw fitness function. In this paper we briefly discuss existing bloat-control techniques, then introduce two new kinds of non-parametric parsimony pressure, Direct and Proportional Tournament. As their names suggest, these techniques are based on simple modifications of tournament selection to consider both size and fitness, but not together as a combined parametric equation. We compare the techniques against, and in combination with, the most popular genetic programming bloat-control technique, Koza-style depth limiting, and show that they are effective in limiting size while still maintaining good best-fitness-of-run results.
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References
Stephen F. Smith. A Learning System Based on Genetic Adaptive Algorithms. PhD thesis, Computer Science Department, University of Pittsburgh, 1980.
Sean Luke. Issues in Scaling Genetic Programming: Breeding Strategies, Tree Generation, and Code Bloat. PhD thesis, Department of Computer Science, University of Maryland, A. V. Williams Building, University of Maryland, College Park, MD 20742 USA, 2000.
John R. Koza. Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge, MA, USA, 1992.
Terence Soule and James A. Foster. Removal bias: a new cause of code growth in tree based evolutionary programming. In 1998 IEEE International Conference on Evolutionary Computation, pages 781–186, Anchorage, Alaska, USA, 5–9 May 1998. IEEE Press.
Terence Soule, James A. Foster, and John Dickinson. Code growth in genetic programming. In John R. Koza, David E. Goldberg, David B. Fogel, and Rick L. Riolo, editors, Genetic Programming 1996: Proceedings of the First Annual Conference, pages 215–223, Stanford University, CA, USA, 28–31 July 1996. MIT Press.
Thomas Haynes. Collective adaptation: The exchange of coding segments. Evolutionary Computation, 6(4):311–338, Winter 1998.
Peter Nordin, Frank Francone, and Wolfgang Banzhaf. Explicitly defined introns and destructive crossover in genetic programming. In Peter J. Angeline and K. E. Kinnear, Jr., editors, Advances in Genetic Programming 2, pages 111–134. MIT Press, Cambridge, MA, USA, 1996.
Donald S. Burke, Kenneth A. De Jong, John J. Grefenstette, Connie Loggia Ramsey, and Annie S. Wu. Putting more genetics into genetic algorithms. Evolutionary Computation, 6(4):387–410, Winter 1998.
Jeffrey K. Bassett and Kenneth A. De Jong. Evolving behaviors for cooperating agents. In International Syposium on Methodologies for Intelligent Systems, pages 157–165, 2000.
Sean Luke and Liviu Panait. Lexicographic parsimony pressure. In W. B. Langdon et al, editor, Proceedings of the Genetic and Evolutionary Computation Conference (GECCO2002). Morgan Kaufmann, 2002.
Byoung-Tak Zhang and Heinz Mühlenbein. Balancing accuracy and parsimony in genetic programming. Evolutionary Computation, 3(1):17–38, 1995.
Stefan Bleuler, Martin Brack, Lothar Thiele, and Eckhart Zitzler. Multiobjective genetic programming: Reducing bloat using spea2. In Proceedings of the 2001 Congress on Evolutionary Computation CEC2001, pages 536–543, COEX, World Trade Center, 159 Samseong-dong, Gangnam-gu, Seoul, Korea, 27–30 May 2001. IEEE Press.
Edwin D. DeJong, Richard A. Watson, and Jordan B. Pollack. Reducing bloat and promoting diversity using multi-objective methods. In Lee Spector, Erik D. Goodman, Annie Wu, W. B. Langdon, Hans-Michael Voigt, Mitsuo Gen, Sandip Sen, Marco Dorigo, Shahram Pezeshk, Max H. Garzon, and Edmund Burke, editors, Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2001), pages 11–18, San Francisco, California, USA, 7–11 July 2001 Morgan Kaufmann.
Aniko Ekart and S. Z. Nemeth. Selection based on the pareto nondomination criterion for controlling code growth in genetic programming. Genetic Programming and Evolvable Machines, 2(1):61–73, March 2001.
Markus Brameier and Wolfgang Banzhaf. Explicit control of diversity and effective variation distance in linear genetic programming. In James A. Foster, Evelyne Lutton, Julian Miller, Conor Ryan, and Andrea G. B. Tettamanzi, editors, Genetic Programming, Proceedings of the 5th European Conference, EuroGP 2002, volume 2278 of LNCS, pages 37–49, Kinsale, Ireland, 3–5 April 2002. Springer-Verlag.
Sean Luke. ECJ 7: An EC system in Java. http://www.cs.umd.edu/projects/plus/ec/ecj/, 2001.
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Luke, S., Panait, L. (2002). Fighting Bloat with Nonparametric Parsimony Pressure. In: Guervós, J.J.M., Adamidis, P., Beyer, HG., Schwefel, HP., Fernández-Villacañas, JL. (eds) Parallel Problem Solving from Nature — PPSN VII. PPSN 2002. Lecture Notes in Computer Science, vol 2439. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45712-7_40
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DOI: https://doi.org/10.1007/3-540-45712-7_40
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