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GECCO
2007
Springer

Learning and anticipation in online dynamic optimization with evolutionary algorithms: the stochastic case

10 years 4 months ago
Learning and anticipation in online dynamic optimization with evolutionary algorithms: the stochastic case
The focus of this paper is on how to design evolutionary algorithms (EAs) for solving stochastic dynamic optimization problems online, i.e. as time goes by. For a proper design, the EA must not only be capable of tracking shifting optima, it must also take into account the future consequences of the evolved decisions or actions. A previous framework describes how to build such EAs in the case of non-stochastic problems. Most real-world problems however are stochastic. In this paper we show how this framework can be extended to properly tackle stochasticity. We point out how this naturally leads to evolving strategies rather than explicit decisions. We formalize our approach in a new framework. The new framework and the various sources of problem–difficulty at hand are illustrated with a running example. We also apply our framework to inventory management problems, an important real–world application area in logistics. Our results show, as a proof of principle, the feasibility and ...
Peter A. N. Bosman, Han La Poutré
Added 07 Jun 2010
Updated 07 Jun 2010
Type Conference
Year 2007
Where GECCO
Authors Peter A. N. Bosman, Han La Poutré
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