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GAPS: A Genetic Programming System

Du Zhang

Department of Computer Science, California State University, Sacramento, CA 95819-6021, USA

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and

Michael D. Kramer

Department of Computer Science, California State University, Sacramento, CA 95819-6021, USA

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https://doi.org/10.1142/S0218213003001198Cited by:1 (Source: Crossref)

Abstract

One of the major approaches in the field of evolutionary computation is genetic programming. Genetic programming tackles the issue of how to automatically create a computer program for a given problem from some initial problem statement. The goal is accomplished by genetically breeding a population of computer programs in terms of genetic operations. In this paper, we describe a genetic programming system called GAPS. GAPS has the following features: (1) It implements the standard generational algorithm for genetic programming with some refinement on controlling introns growth during evolution process and improved termination criteria. (2) It includes an extensible language tailored to the needs of genetic programming. And (3) It is a complete, standalone system that allows for genetic programming tasks to be carried out without requiring other tools such as compilers. Results with GAPS have been satisfactory.

An earlier version of this paper appears in the Proceedings of IEEE COMPSAC 2000. This paper is a substantially revised and extended version.

Keywords:

References

K. A. De Jong, Communications of the ACM 42(11), 51 (1999), DOI: 10.1145/319382.319392. Crossref, Web of Science, Google Scholar
J. H. Holland , Adaptation in Natural and Artificial Systems , 2nd edn. ( University of Michigan Press , Ann Arbor, MI , 1975 ) . Google Scholar
J. H. Holland , Hidden Order: How Adaptation Builds Complexity ( Addison-Wesley Publishing Company , Reading, MA , 1995 ) . Google Scholar
D. E. Goldberg, Communications of the ACM 37(3), 113 (1994), DOI: 10.1145/175247.175259. Crossref, Web of Science, Google Scholar
I. Rechenberg , Evolutionsstrategie'93 ( Frommann Verlag , Stuttgart, Germany , 1994 ) . Google Scholar
H. P. Schwefel , Evolution and Optimum Seeking ( John Wiley & Sons , New York , 1995 ) . Google Scholar
L. Fogel , A. Qwens and M. Walsh , Artificial Intelligence through Simulated Evolution ( John Wiley & Sons , New Yok , 1966 ) . Google Scholar
J. R. Koza , Genetic Programming: On the Programming of Computers by Natural Selection ( MIT Press , Cambridge, MA , 1992 ) . Google Scholar
J. R. Koza , Genetic Programming II: Automatic Discovery of Reusable Programs ( MIT Press , Cambridge, MA , 1994 ) . Google Scholar
J. R. Koza et al. , Genetic Programming III: Darwinian Invention and Problem Solving ( Morgan Kaufmann Publishers, Inc. , San Francisco, CA , 1999 ) . Google Scholar
W. Banzhaf et al. , Genetic Programming: An Introduction ( Morgan Kaufmann Publishers, Inc. , San Francisco, CA , 1998 ) . Crossref, Google Scholar
IEEE Intelligent Systems 15(3), 74 (2000). Google Scholar
L. Spector et al. (eds.) , Advances in Genetic Programming 3 ( MIT Press , Cambridge, MA , 1999 ) . Crossref, Google Scholar
K. E. Kinnear (ed.) , Advances in Genetic Programming ( MIT Press , Cambridge, MA , 1994 ) . Google Scholar
T. M. Mitchell , Machine Learning ( WCB/McGraw-Hill , Boston, MA , 1997 ) . Google Scholar
C. G. Langton (ed.) , Artificial Life II ( Santa Fe Institute , Addison-Wesley , 1991 ) . Google Scholar
J. Cona, Developing a Genetic Programming System, AI Expert, No.2, 1995 . Google Scholar
W. Langdon, Genetic programming bibliography, http://www.cs.bham.ac.uk/~wbl/biblio/gpbibliography.html . Google Scholar
J. Heitkotter and D. Beasley (ed.), The hitch-hiker's guide to evolutionary computation, http://alife.santafe.edu/~joke/encore/www/ . Google Scholar
M. D. Kramer. GAPS: The Genetic Algorithm Programming System, MS degree thesis, Department of Computer Science, California State University, Sacramento, 1996 ftp://ftp.ecs.csus.edu/zhangd/gaps.zip . Google Scholar
M. D. Kramer and D. Zhang, GAPS: a genetic programming, Proc. of 24th IEEE International Conference on Computer Software and Applications (2000) pp. 614–619. Google Scholar
P. J. Angeline, Advances in Genetic Programming, ed. K. E. Kinnear (MIT Press, Cambridge, MA, 1994) pp. 75–98. Google Scholar
P. Nordin, F. Francone and W. Banzhaf, Explicitly defined introns and destructive crossover in genetic programming, Proc. of the Workshop on Genetic Programming: from Theory to Real-World applications (1995) pp. 6–22. Google Scholar
T. Soule, J. A. Foster and J. Dickinson, Code growth in genetic programming, Proc. of the First Annual Conference on Genetic Programming (1996) pp. 215–223. Google Scholar
J. P. Rosca, Analysis of complexity drift in genetic programming, Proc. of the Second Annual Conference on Genetic Programming (1997) pp. 286–294. Google Scholar
W. A. Tackett, Recombination, selection, and the genetic construction of computer programs, Ph.D. thesis, Department of EES, University of Southern California, 1994 . Google Scholar
Discipulus™ genetic programming software, http://www.aimlearning.com . Google Scholar
lil-gp genetic programming system, http://GARAGe.cps.msu.edu/software/lil-gp/lilgpindex. html . Google Scholar
GP related software, http://www.geneticprogramming.com/GPpages/software.html . Google Scholar
Evolutionary computation software, http://www.it.uom.gr/pdp/DigitalLib/EC/ec-soft.html . Google Scholar
EA software packages, http://alife.santafe.edu/~joke/encore/www/Q20.htm . Google Scholar
A. Teller and M. Veloso, Artificial Intelligence 165 (2000), DOI: 10.1016/S0004-3702(00)00023-0. Crossref, Web of Science, Google Scholar