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


The focus of this paper is on how to design evolutionaryalgorithms (EAs) for solving stochastic dynamicoptimization problems online, i.e.~as time goes by.For a proper design, the EA must not only be capableof tracking shifting optima, it must also take intoaccount the future consequences of the evolveddecisions or actions. A previousframework describes how… (More)
DOI: 10.1145/1276958.1277187


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