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Evolutionary processes in modeling and simulation
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Source Spring Simulation Multiconference archive
Proceedings of the 2007 spring simulation multiconference - Volume 3 table of contents
Norfolk, Virginia
SESSION: Model analysis/simulation technology II table of contents
Pages 133-138  
Year of Publication: 2007
ISBN:1-56555-314-4
Authors
Maurice J. Ades  Augusta, GA
Mark S. Burgin  University of California, Los Angeles, Los Angeles, CA
Sponsors
SCS : Society for Modeling and Simulation International
ACM/SIGSIM : Association for Computing Machinery/Special Interest Group on Simulation
Publisher
Bibliometrics
Downloads (6 Weeks): 2,   Downloads (12 Months): 11,   Citation Count: 0
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ABSTRACT

This paper is a continuation of the research on evolutionary optimization and their application to modeling computer simulation [1]. The current research concentrates on the systematization of evolutionary optimization methods and development of new types of evolutionary optimization. Formal representation for genetic operations induces a new typology of genetic algorithms. Computational and generative power of different classes of genetic algorithms and operations is described. The obtained results provide a better understanding of genetic algorithms and genetic simulation systems. At the same time, new types of evolutionary processes described in this paper form a base for practical techniques and program systems working in applications where it is necessary to organize optimization concurrently with system functioning.


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
Ades, M. J. and Burgin, M. Multilevel Hierarchical Genetic Algorithms, Proc. of the 2006 Spring Simulation MultiConference, 2006, Huntsville, Alabama, 223--228
 
2
Büchi, J. R. Weak second order arithmetic and finite automata, Z. Math. Logic Grudl. Math., v. 6, 1960, 66--92
 
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Burgin, M. Topological Algorithms, in Proc. ISCA 16th International Conference "Computers and their Applications", Seattle, Washington, 2001, 61--64
 
5
Burgin, M. Superrecursive Algorithms, Springer, New York, 2005
 
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Burgin, M. Measuring Power of Algorithms, Programs, and Automata, in "Artificial Intelligence and Computer Science", Nova Science Publishers, New York, 1--61
 
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Burgin, M., Karplus, W., and Liu, D. Branching Simulation and Prediction, Proc. of the 2006 Spring Simulation MultiConference, Washington, 2000, 47--52
 
8
Everett, H. 'Relative State' Formulation of Quantum Mechanics, Reviews of Modern Physics, v. 29, 1957, 454--462
 
9
Fogel, L. J., Owens, A. J., and Walsh, M. J. Artificial Intelligence through Simulated Evolution, John Wiley, New York, 1966
 
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Collaborative Colleagues:
Maurice J. Ades: colleagues
Mark S. Burgin: colleagues