CMU Artificial Intelligence Repository
GENESIS: GENEtic Search Implementation System
areas/genetic/ga/systems/genesis/
This directory contains GENEtic Search Implementation System
(GENESIS). GENESIS is system for function optimization based on
genetic search techniques. Since genetic algorithms are task
independent optimizers, the user must provide only an "evaluation"
function which returns a value when given a particular point in the
search space.
This version offers several enhancements over previous versions that
should make the system much more user friendly. The major improvement
is a user-level representation that allows the user to think about the
genetic structures as vectors of real numbers, rather than bit strings.
This level of representation should make the application of GENESIS to
new problems easier than ever. A number of new options have been added,
including: a display mode that includes an interactive user interface,
the option to maximize or minimize the objective function, the choice of
rank-based or proportional selection algorithm, and an option to use a
Gray code as a transparent lower level representation.
The purpose of making this system available is to encourage the
experimental use of genetic algorithms on realistic optimization
problems, and thereby to identify the strengths and weaknesses of
genetic algorithms.
Version: 5.0 (October 1990)
Requires: C
Copying: Copyright (c) 1986, 1990 by John J. Grefenstette
Use and copying permitted for educational and research
purposes. All other rights reserved.
CD-ROM: Prime Time Freeware for AI, Issue 1-1
Author(s): John Grefenstette
Keywords:
Authors!Grefenstette, C!Code, Function Optimization, GENESIS,
Genetic Algorithms
References:
1. James E. Baker, "Reducing bias and inefficiency in the selection
algorithm," in Genetic Algorithms and Their Applications: Proc. 2nd
Intl. Conf., ed. J. J. Grefenstette, pp. 14-21, LEA, Cambridge,
MA, July 1987.
2. A. D. Bethke, Genetic algorithms as function optimizers, Ph. D.
Thesis, Dept. Computer and Communication Sciences, Univ. of Michigan,
1981.
3. A. Brindle, Genetic algorithms for function optimization, Ph. D.
Thesis, Computer Science Dept., Univ. of Alberta, 1981.
4. K. A. DeJong, Analysis of the behavior of a class of genetic
adaptive systems, Ph. D. Thesis, Dept. Computer and Communication
Sciences, Univ. of Michigan, 1975.
5. K. A. DeJong, "Adaptive system design: a genetic approach," IEEE
Trans. Syst., Man, and Cyber., vol. SMC-10, no. 9, pp. 566-574,
Sept. 1980.
6. D. R. Frantz, Non-linearities in genetic adaptive search, Ph. D.
Thesis, Dept. Computer and Communication Sciences, Univ. of Michigan,
1972.
7. J. H. Holland, Adaptation in Natural and Artificial Systems, Univ.
Michigan Press, Ann Arbor, 1975.
8. R. B. Hollstien, Artificial genetic adaptation in computer control
systems, Ph. D. Thesis, Dept. Computer and Communication Sciences,
Univ. of Michigan, 1971.
9. S. F. Smith, "Flexible learning of problem solving heuristics
through adaptive search," Proc. 8th Intl. J. Conf. Artif. Intel.
(IJCAI), Aug. 1983.
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