Parallel Genetic Programming on a Network of Transputers

This paper describes the parallel implementation of genetic programming in the C programming language using a PC 486 type computer running Windows acting as a host and a network of transputers acting as processing nodes. Using this approach, researchers of genetic algorithms and genetic programming can acquire computing power that is intermediate between the power of currently available workstations and that of supercomputers at a cost that is intermediate between the two. A comparison is made of the computational effort required to solve the problem of symbolic regression of the Boolean even-5-parity function with different migration rates. Genetic programming required the least computational effort with migration rate at 5%; however, there was little difference in performance for migration rates between 1% and 8%. Moreover, this computational effort required for these migration rates was less than that required for solving the problem with a serial computer and a panmictic population of the same size. That is, apart from the nearly linear speed-up in executing a fixed amount of code inherent in the parallel implementation of genetic programming, parallelization delivered more than linear speed-up in solving the problem using genetic programming.