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Generating novel tactics through evolutionary computation
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Volume 9 ,  Issue 2  (Fall 1998) table of contents
Pages: 8 - 14  
Year of Publication: 1998
ISSN:0163-5719
Authors
V. William Porto  Natural Selection, Inc., La Jolla, CA
David B. Fogel  Natural Selection, Inc., La Jolla, CA
Lawrence J. Fogel  Natural Selection, Inc., La Jolla, CA
Publisher
ACM  New York, NY, USA
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ABSTRACT

Simulated evolution on a computer can provide a means for generating appropriate tactics in real-time combat scenarios. Individual unit or higher level organizations, such as tanks and platoons, can use evolutionary computation to adapt to the current and projected situations. In this article, we briefly review current knowledge in evolutionary algorithms and offer an example of applying these techniques to generate adaptive behavior in a platoon-level engagement of tanks in which the mission of one platoon is changed on-the-fly.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
1
Angeline, P. J. Subtree crossover: Building block engine or macromutation? In J. R. Koza, K. Deb, M. Dorigo, D. B. Fogel, M. H. Garzon, H. Iba, and R. L. Riolo, editors, Proceedings of the 2nd Annual Conference on Genetic Programming. Morgan Kaufmann, San Francisco, 1997, 9--17.
 
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Thomas Bäck , Hans-Paul Schwefel, An overview of evolutionary algorithms for parameter optimization, Evolutionary Computation, v.1 n.1, p.1-23, Spring 1993
 
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Chellapilla, K. A preliminary investigation into evolving modular programs without subtree crossover. In J. R. Koza, W. Banzhaf, K. Chellapilla, K. Deb, M. Dorigo, D. B. Fogel, M. H. Garzon, D. E. Goldberg, H. Iba, and R. L. Riolo, editors, Proceedings of the 3rd Annual Genetic Programming Conference. Morgan Kaufmann, San Francisco, 1998, 23--31.
 
5
Darwin, C. On the Origin of Species by Means of Natural Selection or the Preservations of Favored Races in the Struggle for Life. John Murray, London, 1859.
 
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Davis, L., editor. Handbook of Genetic Algorithms. Van Nostrand Reinhold, New York, 1991.
 
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Fogel, D. B. Asymptotic convergence properties of genetic algorithms and evolutionary programming. Cybernetics and Systems, 1994, 25(3):389--407.
 
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Fogel, D. B. and Ghozeil, A. A note on representations and variation operators. IEEE Transactions on Evolutionary Computation, 1997, 1(2):159--161.
 
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Fogel, D. B. and Stayton, L. C. On the effectiveness of crossover in simulated evolutionary optimization. BioSystems, 1994, 32(3):171--182.
 
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Fuchs, M. Crossover versus mutation: An empirical and theoretical case study. In J. R. Koza, W. Banzhaf, K. Chellapilla, K. Deb, M. Dorigo, D. B. Fogel, M. H. Garzon, D. E. Goldberg, H. Iba, and R. L. Riolo, editors, Proceedings of the 3rd Annual Genetic Programming Conference. Morgan Kaufmann, San Francisco, 1998, 78--85.
 
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Luke, S. and Spector, L. A revised comparison of crossover and mutation in genetic programming. In J. R. Koza, W. Banzhaf, K. Chellapilla, K. Deb, M. Dorigo, D. B. Fogel, M. H. Garzon, D. E. Goldberg, H. Iba, and R. L. Riolo, editors, Proceedings of the 3rd Annual Genetic Programming Conference. Morgan Kaufmann, San Francisco, 1998, 208--213.
 
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Macready, W. G. and Wolpert, D. H. Bandit problems and the exploration/exploitation tradeoff. IEEE Transactions on Evolutionary Computation, 1998, 2(1), in press.
 
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Rudolph, G. Reflections on bandit problems and selection methods in uncertain environments. In T. Bäck, editor, Proceedings of the 7th International Conference on Genetic Algorithms, Morgan Kaufmann, San Francisco, 1997, 166--173.
Collaborative Colleagues:
V. William Porto: colleagues
David B. Fogel: colleagues
Lawrence J. Fogel: colleagues