- author = "Jack Y. B. Lee and P. C. Wong",
- title = "The effect of function noise on GP efficiency",
- booktitle = "Progress in Evolutionary Computation",
- publisher = "Springer-Verlag",
- year = "1995",
- editor = "Xin Yao",
- volume = "956",
- series = "Lecture Notes in Artificial Intelligence",
- pages = "1--16",
- address = "Heidelberg, Germany",
- keywords = "genetic algorithms, genetic programming",
- isbn13 = "978-3-540-60154-8",
-
DOI = "
doi:10.1007/3-540-60154-6_43",
- 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 counter noise 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-Test cases 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.",
- size = "16 pages",
-
notes = "Artificial ant on Santa Fe Trail with noisy
IfFoodAhead GP does poorly even with small amounts of
with noise. Sometimes population abandons use of
IfFoodAhead entirely (what else could it do?)
",
- affiliation = "The Chinese University of Hong Kong Advanced Network Systems Laboratory Department of Information Engineering Hongkong Hongkong",
