Please use this identifier to cite or link to this item:
|Type:||Artigo de evento|
|Title:||A Dynamic Artificial Immune Algorithm Applied To Challenging Benchmarking Problems|
|Author:||De Franca F.O.|
Von Zuben F.J.
|Abstract:||In many real-world scenarios, in contrast to standard benchmark optimization problems, we may face some uncertainties regarding the objective function. One source of these uncertainties is a constantly changing environment in which the optima change their location over time. New heuristics or adaptations to already available algorithms must be conceived in order to deal with such problems. Among the desirable features that a search strategy should exhibit to deal with dynamic optimization are diversity maintenance, a memory of past solutions, and a multipopulation structure of candidate solutions. In this paper, an immune-inspired algorithm that presents these features, called dopt-aiNet, is properly adapted to deal with six newly proposed benchmark instances, and the obtained results are outlined according to the available specifications for the competition at the Congresson Evolutionary Computation 2009. © 2009 IEEE.|
|Appears in Collections:||Unicamp - Artigos e Outros Documentos|
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