Makoto Tanji
ABSTRACT
This paper describes a new program evolution method named PORTS (Program Optimization by Random Tree Sampling) which is motivated by the idea of preservation and control of tree fragments. We hypothesize that to reconstruct building blocks efficiently, tree fragments of any size should be preserved into the next generation, according to their differential fitnesses. PORTS creates a new individual by sampling from the promising trees by traversing and transition between trees instead of subtree crossover and mutation. Because the size of a fragment preserved during a generation update follows a geometric distribution, merits of the method are that it is relatively easy to predict the behavior of tree fragments over time and to control sampling size, by changing a single parameter. Our experimental results on three benchmark problems show that the performance of PORTS is competitive with SGP (Simple Genetic Programming). And we observed that there is a significant difference of fragment distribution between PORTS and simple GP.
- Lawrence Beadle and Colin G. Johnson. Semantically driven crossover in genetic programming. In 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence), 2008.Google Scholar
Cross Ref
- Jason M. Daida, Robert R. Bertram, Stephen A. Stanhope, Jonathan C. Khoo, Shahbaz A. Chaudhary, Omer A. Chaudhri, and John A. II Polito. What makes a problem gp-hard? analysis of a tunably difficult problem in genetic programming. Genetic Programming and Evolvable Machines, Volume 2, Number 2:165--191, 2001. Google ScholarDigital Library
- Steven Gustafson, Edmund K. Burke, and Graham Kendall. Genetic Programming, chapter Sampling of Unique Structures and Behaviours in Genetic Programming, pages 279--288. Springer Berlin / Heidelberg, 2004.Google Scholar
- Hoang Tuan Hao, Nguyen Xuan Hoai, Nguyen Thi Hien, RI McKay, and Daryl Essam. Ordertree: A new test problem for genetic programming. In GECCO '06: Proceedings of the 8th annual conference on Genetic and evolutionary computation, 2006. Google ScholarDigital Library
- Kim Harries and Peter Smith. Exploring alternative operators and search strategies in genetic programming. In Genetic Programming 1997: Proceedings of the Second Annual Conference, 1997.Google Scholar
- J. R. Koza. Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, 1992. Google ScholarDigital Library
- J. R. Koza. Genetic Programming II: Automatic Discovery of Reusable Programs. MIT Press, 1994. Google ScholarDigital Library
- Moshe Looks. On the behavioral diversity of random programs. In GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation, 2007. Google ScholarDigital Library
- Hammad Majeed and Conor Ryan. Using context-aware crossover to improve the performance of gp. In GECCO '06: Proceedings of the 8th annual conference on Genetic and evolutionary computation, 2006. Google ScholarDigital Library
- M. Mitchell, S. Forrest, and J.H. Holland. The royal road for genetic algorithms: Fitness landscapes and ga performance. In Towards a Practice of Autonomous Systems: Proceedings of the First European Conference on Artificial Life, 1992.Google Scholar
- Una-May O'Reilly and Franz Oppacher. Program search with a hierarchical variable length representation: Genetic programming, simulated annealing and hill climbing. Parallel Problem Solving from Nature - PPSN III, 866:397--406, 1994. Google ScholarDigital Library
- Riccardo Poli, Nicholas Freitag McPhee, and Nicholas Freitag McPhee. The impact of population size on code growth in gp: Analysis and empirical validation. In GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation, 2008. Google ScholarDigital Library
- Bill Punch, Doug Zongker, and Erik Goodman. Advances in genetic programming: volume 2, chapter The royal tree problem, a benchmark for single and multiple population genetic programming, pages 299--316. MIT Press, 1996. Google ScholarDigital Library
- Y. Shan, R.I. McKay, and et al. R. Baxter. Grammar model-based program evolution. In Proceedings of The Congress on Evolutionary Computation, 2004.Google Scholar
- Terence Soule and Robert B. Heckendorn. An Analysis of the Causes of Code Growth in Genetic Programming, pages 283--309. Springer-Netherlands, 2004. Google ScholarDigital Library
- Hasegawa Yoshihiko and Hitoshi Iba. A bayesian network apporach to program generation. IEEE Transaction on Evolutionary Computation, 12, No. 6:750 to 764, 2008.Google ScholarDigital Library
Index Terms
- Program optimization by random tree sampling
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