Evolutionary Algorithms for Dynamic Optimization Problems: An approach using Evolutionary Theory and the Incident Edge Model

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“Evolutionary Algorithms for Dynamic Optimization Problems: An approach using Evolutionary Theory and the Incident Edge Model” by R. Santana, A. Ochoa, and M. R. Soto. In Proceedings of the Genetic and Evolutionary Computation Conference GECCO-1999, Workshop Program, (A. S. Wu, ed.), (Orlando, FL), 1999, pp. 149-152.

Abstract

In this paper we analyze the use of Evolutionary Algorithms (EAâÂÂs) for Dynamic Optimization Problems (DOP). We show how research on Evolutionary Theory can throw light to the question of characterizing dynamic problems. We present arguments in favor of using fitness function models as benchmark for the study of DOP. As an example we present the Incident Edge Model, and discuss how different kinds of dynamics for problems defined on graphs can be translated to the model. Finally we apply an EA, the Constraint Univariate Marginal Distribution Algorithm, for the problem of finding the spanning trees of graphs that change through time. We show that efficient EAâÂÂs should be able to employ information about changes in the environment to guide the search.

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BibTeX entry:

@inproceedings{Santana_et_al:1999a,
   author = {R. Santana and A. Ochoa and M. R. Soto},
   editor = {A. S. Wu},
   title = {Evolutionary {A}lgorithms for {D}ynamic {O}ptimization
	{P}roblems: An approach using {E}volutionary {T}heory and the
	{I}ncident {E}dge {M}odel},
   booktitle = {Proceedings of the Genetic and Evolutionary Computation
	Conference {GECCO}-1999, Workshop Program},
   pages = {149--152},
   publisher = {Morgan Kaufmann Publishers, San Francisco, CA},
   address = {Orlando, FL},
   year = {1999}
}

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