Inproceedings,
A parallel implementation of genetic programming that achieves super-linear performance
Proceedings of the International Conference on Parallel and Distributed Processing Techniques and Applications, III, page 1163--1174. Sunnyvale, CSREA, (9-11 August 1996)
Abstract
This paper describes the successful parallel
implementation of genetic programming on a network of
processing nodes using the transputer architecture.
With 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
intermediate cost. This approach is illustrated by a
comparison of the computational effort required to
solve a benchmark problem. Because of the decoupled
character of genetic programming, our approach achieved
a nearly linear speed up from parallelization. In
addition, for the best choice of parameters tested, the
use of subpopulations delivered a super linear speed-up
in terms of the ability of the algorithm to solve the
problem. Several examples are also presented where the
parallel genetic programming system evolved solutions
that are competitive with human performance on the same
problem.
