Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/66826
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Type: Conference paper
Title: Evolutionary algorithms and dynamic programming
Author: Doerr, B.
Eremeev, A.
Horoba, C.
Neumann, F.
Theile, M.
Citation: Proceedings of the 11th Annual conference on Genetic and Evolutionary Computation: GECCO '09, pp.771-777
Publisher: ACM Press
Publisher Place: New York
Issue Date: 2009
ISBN: 9781605583259
Conference Name: Genetic and Evolutionary Computation Conference (11th : 2009 : Montreal, Canada)
Editor: Rothlauf, F.
Statement of
Responsibility: 
Benjamin Doerr, Anton Eremeev, Christian Horoba, Frank Neumann and Madeleing Theile
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.
Keywords: Combinatorial optimization
dynamic programming
evolutionary algorithms
Rights: Copyright 2009 ACM
DOI: 10.1145/1569901.1570008
Published version: http://dx.doi.org/10.1145/1569901.1570008
Appears in Collections:Aurora harvest 5
Computer Science publications

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