Evolutionary algorithms and dynamic programming

Authors:
Benjamin Doerr;Anton Eremeev;Christian Horoba;Frank Neumann;Madeleine Theile
Affiliations:
Max-Planck-Institut für Informatik, Saarbruecken, Germany;Omsk Branch of Sobolev Institute of Mathematics, Omsk, Russian Fed.;TU Dortmund, Dortmund, Germany;Max-Planck-Institut für Informatik, Saarbruecken, Germany;TU Berlin, Berlin, Germany
Venue:
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Year:
2009

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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.