Incollection,
Parallel Genetic Programming: A Scalable Implementation Using The Transputer Network Architecture
Advances in Genetic Programming 2, chapter 16, MIT Press, Cambridge, MA, USA, (1996)
Abstract
This chapter describes the parallel implementation of
genetic programming in the C programming language using
a PC type computer (running Windows) acting as a host
and a network of processing nodes using the transputer
architecture. 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. This approach is illustrated by a comparison
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 5% 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, the use of distributed
sub-populations with only limited migration delivered
more than linear speed-up in solving the problem.
