%0 Book Section %1 wong:1996:aigp2 %A Wong, Man Leung %A Leung, Kwong Sak %B Advances in Genetic Programming 2 %C Cambridge, MA, USA %D 1996 %E Angeline, Peter J. %E Kinnear, Jr., K. E. %I MIT Press %K algorithms, genetic programming %P 221--240 %T Evolving Recursive Functions for the Even-Parity Problem Using Genetic Programming %X One of the most important and challenging areas of research in evolutionary algorithms is to investigate ways to successfully apply evolutionary algorithms to larger and more complicated problems. One approach to make a given problem more tractable is to discover problem representations automatically. Koza (1993) uses the even-n-parity problem to demonstrate extensively that his approach of Automatic Function Definition (ADF) can facilitate the solution of the problem. Unfortunately, the solutions found by GP with ADF can only solved the problem for a particular value of n. If a different value of n is used, GP with ADF must be used again to find other programs that can solve the new even-n-parity problem. Clearly, the solution found is not general enough to solve all even-n-parity problem for n greater than or equal to zero. In this paper, we apply GGP (Generic Genetic Programming) to evolve general recursive functions for the even-n-parity problem. GGP is very flexible and programs in various programming languages can be acquired. Moreover, it is powerful enough to represent context-sensitive information and domain-dependent knowledge. This knowledge can be used to accelerate the learning speed and/or improve the quality of the programs induced. A number of experiments have been performed to determine the impact of domain-specific knowledge on the speed of learning. %& 11 %@ 0-262-01158-1

@incollection{wong:1996:aigp2, abstract = {One of the most important and challenging areas of research in evolutionary algorithms is to investigate ways to successfully apply evolutionary algorithms to larger and more complicated problems. One approach to make a given problem more tractable is to discover problem representations automatically. Koza (1993) uses the even-n-parity problem to demonstrate extensively that his approach of Automatic Function Definition (ADF) can facilitate the solution of the problem. Unfortunately, the solutions found by GP with ADF can only solved the problem for a particular value of n. If a different value of n is used, GP with ADF must be used again to find other programs that can solve the new even-n-parity problem. Clearly, the solution found is not general enough to solve all even-n-parity problem for n greater than or equal to zero. In this paper, we apply GGP (Generic Genetic Programming) to evolve general recursive functions for the even-n-parity problem. GGP is very flexible and programs in various programming languages can be acquired. Moreover, it is powerful enough to represent context-sensitive information and domain-dependent knowledge. This knowledge can be used to accelerate the learning speed and/or improve the quality of the programs induced. A number of experiments have been performed to determine the impact of domain-specific knowledge on the speed of learning.}, added-at = {2008-06-19T17:35:00.000+0200}, address = {Cambridge, MA, USA}, author = {Wong, Man Leung and Leung, Kwong Sak}, biburl = {https://www.bibsonomy.org/bibtex/297be2b3297e26cfd6331ac13517638e3/brazovayeye}, booktitle = {Advances in Genetic Programming 2}, chapter = 11, editor = {Angeline, Peter J. and {Kinnear, Jr.}, K. E.}, interhash = {c75bbd3e0e9c8242aeaeecc5bcaa97d4}, intrahash = {97be2b3297e26cfd6331ac13517638e3}, isbn = {0-262-01158-1}, keywords = {algorithms, genetic programming}, pages = {221--240}, publisher = {MIT Press}, timestamp = {2008-06-19T17:54:21.000+0200}, title = {Evolving Recursive Functions for the Even-Parity Problem Using Genetic Programming}, year = 1996 }