One of the most important and challenging areas of research in evolutionary algorithms is the investigation of ways to successfully apply evolutionary algorithms to larger and more complicated problems. In this paper, we apply GGP (Generic Genetic Programming) to evolve general recursive functions for the even-n-parity problem from noisy training examples. GGP is very flexible and programs in various programming languages can be acquired. Moreover, it is powerful enough to handle context-sensitive information and domain-dependent knowledge. A number of experiments have been performed to determine the impact of noise in training examples on the speed of learning.
|Title of host publication||Proceedings of the 1st annual conference on genetic programming|
|Publication status||Published - Jul 1996|
|Event||1st annual conference on genetic programming - Stanford University, California, United States|
Duration: 28 Jul 1996 → 31 Jul 1996
|Conference||1st annual conference on genetic programming|
|Period||28/07/96 → 31/07/96|
WONG, M. L., & LEUNG, K. S. (1996). Learning Recursive Functions from Noisy Examples using Generic Genetic Programming. In Proceedings of the 1st annual conference on genetic programming (pp. 238-246). MIT Press. https://doi.org/10.5555/1595536.1595566