Abstract
Genetic Programming (GP) has been applied to many problems and there are indications [1,2,3] that GP is potentially useful in evolving algorithms for problem solving. This paper investigates one problem with algorithmic evolution using GP — Function Noise. We show that the performance of GP could be severely degraded even in the presence of minor noise in the GP functions. We investigated two counternoise schemes, Multi-Sampling Function and Multi-Testcases. We show that the Multi-Sampling Function scheme can reduce the effect of noise in a predictable way while the Multi-Testcases scheme evolves radically different program structures to avoid the effect of noise. Essentially, the two schemes lead the GP to evolve into different “approaches” to solving the same problem.
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References
J. Koza. Genetic Programming. MIT Press. Cambridge, MA.
Kinnear, Kenneth E. Jr. Evolving a sort: Lessons in genetic programming. 1993 IEEE International Conference on Neural Networks, San Francisco. Piscataway, NJ: IEEE 1993. Volume 2. Pages 881–888. 1993a.
Kinnear, Kenneth E. Jr. Generality and difficulty in genetic programming: Evolving a sort. In Forrest, Stephanie (editor). Proceedings of the Fifth International Conference on Genetic Algorithms. San Mateo, CA: Morgan Kaufmann Publishers Inc. Pages 287–294. 1993b.
Unix Programmer's Guide. Online man pages.
Tackett, Walter Alden. Genetic programming for feature discovery and image discrimination. In Forrest, Stephanie (editor). Proceedings of the Fifth International Conference on Genetic Algorithms. San Mateo, CA: Morgan Kaufmann Publishers Inc. Pages 303–309. 1993a.
The genetic planner: The automatic generation of plans for a mobile robot via genetic programming. Proceedings of the Eighth IEEE International Symposium on Intelligent Control. Pages 190–195.
Unix Programmer's Guide. Online man pages.
Handley, Simon. The automatic generation of plans for a mobile robot via genetic programming with automatically defined functions. In Proceedings of the 1993 International Simulation Technology Multiconference (SimTec '93).
Handley, Simon. The automatic generation of plans for a mobile robot via genetic programming with automatically defined functions. In Kinnear, Kenneth E. Jr. (editor). Advances in Genetic Programming. Cambridge: The MIT Press 1994.
Reynolds, Craig W. An evolved vision-based behavioral model of coordinated group motion. In Meyer, Jean-Arcady, Roitblat, Herbert L. and Wilson, Stewart W. (editors). From Animals to Animats 2: Proceedings of the Second International Conference on Simulation of Adaptive Behavior. Cambridge, MA: The MIT Press. Pages 384–392.
Reynolds, Craig W. Evolution of obstacle avoidance behavior: Using noise to promote robust solutions. In Kinnear, Kenneth E. Jr. (editor). Advances in Genetic Programming. Cambridge: The MIT Press.
Reynolds, Craig W. An evolved vision-based model of obstacle avoidance behavior. In Langton, Christopher (editor). Artificial Life III.
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© 1995 Springer-Verlag Berlin Heidelberg
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Lee, J.Y.B., Wong, P.C. (1995). The effect of function noise on GP efficiency. In: Yao, X. (eds) Progress in Evolutionary Computation. EvoWorkshops EvoWorkshops 1993 1994. Lecture Notes in Computer Science, vol 956. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-60154-6_43
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DOI: https://doi.org/10.1007/3-540-60154-6_43
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