Abstract
This report 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 an 8% migration rate.
Moreover, this computational effort 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.
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