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The effect of function noise on GP efficiency

  • Jack Y. B. Lee
  • P. C. Wong
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 956)

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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Copyright information

© Springer-Verlag Berlin Heidelberg 1995

Authors and Affiliations

  • Jack Y. B. Lee
    • 1
  • P. C. Wong
    • 1
  1. 1.Advanced Network Systems Laboratory Department of Information EngineeringThe Chinese University of Hong KongHongkong

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