A parallel implementation of genetic programming that achieves super-linear performance

Authors:
David Andre;John R. Koza
Affiliations:
Computer Science Division, University of California at Berkeley, Berkeley, CA 94720-1776, USA;Computer Science Department, Stanford University, Stanford, CA 94305, USA
Venue:
Information Sciences: an International Journal
Year:
1998

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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.