SGA-Cube: A Simple Genetic Algorithm for 
nCUBE 2 Hypercube Parallel Computers

Jeff A. Earickson
Alabama Supercomputer Network

Robert E. Smith
The University of Alabama

and

David E. Goldberg
The University of Illinois

TCGA Report No. 91005
The Clearinghouse for Genetic Algorithms
The University of Alabama
Department of Engineering Mechanics
Tuscaloosa, AL 35487

SGA-Cube is a C-language translation of the original
Pascal SGA code presented by Goldberg (1989)
with modifications to allow execution on the nCUBE 2 Hypercube Parallel
Computer. When run on the nCUBE 2, SGA-Cube takes advantage of the 
hypercube architecture, and is scalable to any hypercube dimension. 
The hypercube implementation is modular, so that the algorithm for
exploiting parallel processors can be easily modified.
In addition to its parallel capabilities, SGA-Cube can be
compiled on various serial computers via compile-time options.
In fact, when compiled on a serial computer, SGA-Cube is essentially
identical to SGA-C (Smith, Goldberg, & Earickson, 1991).
SGA-Cube has been nominally tested on a Sun 470 workstation, 
a Vax Ultrix system, a Cray X-MP/24 running UNICOS 5.1, and the nCUBE 2.
A technical report (in PostScript file sga-cube.ps) 
is included as a concise introduction to the SGA-Cube distribution.
It is presented with the assumptions that the reader has a general 
understanding of Goldberg's original Pascal SGA code, and a good 
working knowledge of the C programming language. The report begins with 
an outline of the files included in the SGA-Cube distribution, and 
the routines they contain. The outline is followed by a discussion of the 
significant features of SGA-Cube. The report concludes with a
discussion of routines that must be altered to implement one's own application in SGA-Cube.

The authors are interested in the comments, criticisms, and bug reports
from SGA-Cube users, so that the code can be refined for
easier use in subsequent versions.
Please email your comments to @ua1ix.ua.edu:rob@galab2.mh.ua.edu,
or write to TCGA:

The Clearinghouse for Genetic Algorithms
The University of Alabama
Department of Engineering Mechanics
P.O. Box 870278
Tuscaloosa, Alabama 35487


Goldberg, D.E. (1989). Genetic algorithms in search, optimization, and
machine learning. Reading, MA: Addison-Wesley. 

Smith, R.E., Goldberg, D. E., & Earickson, J. (1991). SGA-C: A C-language
implementation of a simple genetic algorithm (TCGA Report No. 91002).
Tuscaloosa: The University of Alabama, The Clearinghouse for Genetic 
Algorithms.

