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Enhanced forma analysis of permutation problems
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Genetic And Evolutionary Computation Conference archive
Proceedings of the 9th annual conference on Genetic and evolutionary computation table of contents
London, England
SESSION: Formal theory: papers table of contents
Pages: 923 - 930  
Year of Publication: 2007
ISBN:978-1-59593-697-4
Authors
Tao Gong  City University of London, London, United Kingdom
Andrew L. Tuson  City University of London, London, United Kingdom
Sponsors
SIGEVO: ACM Special Interest Group on Genetic and Evolutionary Computation
ACM: Association for Computing Machinery
Publisher
ACM  New York, NY, USA
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ABSTRACT

Forma analysis provides an approach to formally derive domain specific operators based on domain-independent operator templates by manipulating a set of equivalence relations (i.e., the basis), which is used to describe the search space. In the case of permutation problems, where the basis is highly constrained, the declarative nature of forma analysis encounters some difficulties which give rise to some additional issues, such as the interpretation of declarative constraints and the complexity of the application of operator. This paper aims to address these issues by introducing Enhanced Forma Analysis that explores a broader view of forma analysis by using ideas from constraint satisfaction.


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
T. Bäck. Evolutionary Computation 2:Advanced Algorithms and Operators. Institute of Physics (IOP) Publishing, Bristol, UK, 2000.
 
2
T. Bäck, D. Fogel, and Z. Michalewicz. Evolutionary Computation 1:Basic Algorithms and Operators. Institute of Physics (IOP)Publishing, Bristol, UK, 2000.
 
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4
M. DeLeon. A study of su. cient conditions for hamiltonian cycles. Technical report, Department of Mathematics and Computer Science, Seton Hall University, NJ, USA, 2000.
 
5
T. Gong and A. L. Tuson. Formal descriptions of real parameter optimisation. In Proceedings of IEEE Congress on Evolutionary Computation 2006, Vancouver , pages 2119--2126. IEEE Press, 2006.
 
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N. Radcliffe. The algebra of genetic algorithms. Annals of Maths and Artificial Intelligence , 10:339--384, 1994.
 
8
T. Starkweather, S. McDaniel, K. Mathias, C. Whitley, and D. Whitley. A comparison of genetic sequencing operators. In R. Belew and L. Booker, editors, Proceedings of the Fourth International Conference on Genetic Algorithms , pages 69--76. Morgan Kaufmann, 1991.
 
9
P. D. Surry. A Prescriptive Formalism for Constructing Domain-specific Evolutionary Algorithm. PhD thesis, University of Edinburgh, Edinburgh, Scotland, UK, 1998.
 
10
E. P. K. Tsang. Foundations of Constraint Satisfaction. Academic Press, London, UK, 1993.
 
11
A. L. Tuson. No optimization without representation: a knowledge based systems view of evolutionary/neighbourhood search optimization. PhD thesis, University of Edinburgh, Edinburgh, 1999.


Collaborative Colleagues:
Tao Gong: colleagues
Andrew L. Tuson: colleagues