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Genetic Programming in Mathematica




Genetic Programming is generally considered to be a branch of Artificial Intelligence. it is a generalisation of Genetic Algorithms. together with other methods, these fall into the sub-category called Automatic Programming.

the basic theory is that it should be possible to have programs create other programs. this would mean that some tasks would not have to coded manually. instead a computer could be "asked" to generate a solution to a problem in the form of a computer program.

in Genetic Programming, the solution is evolved by applying the principles of evolution and Darwinian survival of the fittest. a group of random solutions is first created and these are then changed by randomly breaking up and exchanging portions of the solutions. the degree to which a solution solves a problem correctly determines that solution's ability to interact with other solutions. eventually the group of solutions produces one that is acceptable.

i completed my Masters thesis in 1997.

Team Members

hussein, Miloslav Hajek


Suleman, H., and M. Hajek (1996). Parallelism: An Effective Genetic Programming Implementation on Low-powered Mathematica Workstations, in Proceedings of The 1996 National Research and Development Conference: SAICSIT 1996, 26-27 September, University of Natal, Durban.

Suleman, H. (1997). Genetic Programming in Mathematica, M.Sc. thesis, University of Durban-Westville. Available http://www.husseinsspace.com/research/publications/gpinmath.pdf

last updated on : Fri Jul 11 03:16:48 2008