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Evolutionary algorithms and dynamic programming
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Genetic And Evolutionary Computation Conference archive
Proceedings of the 11th Annual conference on Genetic and evolutionary computation table of contents
Montreal, Québec, Canada
SESSION: Track 9: genetic algorithms table of contents
Pages 771-778  
Year of Publication: 2009
ISBN:978-1-60558-325-9
Authors
Benjamin Doerr  Max-Planck-Institut für Informatik, Saarbruecken, Germany
Anton Eremeev  Omsk Branch of Sobolev Institute of Mathematics, Omsk, Russian Fed.
Christian Horoba  TU Dortmund, Dortmund, Germany
Frank Neumann  Max-Planck-Institut für Informatik, Saarbruecken, Germany
Madeleine Theile  TU Berlin, Berlin, Germany
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

Recently, it has been proven that evolutionary algorithms produce good results for a wide range of combinatorial optimization problems. Some of the considered problems are tackled by evolutionary algorithms that use a representation, which enables them to construct solutions in a dynamic programming fashion. We take a general approach and relate the construction of such algorithms to the development of algorithms using dynamic programming techniques. Thereby, we give general guidelines on how to develop evolutionary algorithms that have the additional ability of carrying out dynamic programming steps.


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.

 
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R. E. Bellman and S. E. Dreyfus. Applied Dynamic Programming. Princeton University Press, 1962.
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Collaborative Colleagues:
Benjamin Doerr: colleagues
Anton Eremeev: colleagues
Christian Horoba: colleagues
Frank Neumann: colleagues
Madeleine Theile: colleagues