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

P.A.N. Bosman, J.A. Poutré, La

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

37 Citations (Scopus)

Abstract

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 to build such EAs in thecase of non-stochastic problems. Most real-worldproblems however are stochastic. In this paper weshow how this framework can be extended to properlytackle stochasticity. We point out how thisnaturally leads to evolving strategiesrather than explicit decisions. We formalizeour approach in a new framework. The newframework and the various sourcesof problem-difficulty at hand are illustratedwith a running example. We also apply ourframework to inventory management problems, an importantreal-world application area in logistics. Our results show,as a proof of principle, the feasibility and benefitsof our novel approach.
Original languageEnglish
Title of host publicationProceedings of the 9th annual conference on Genetic and evolutionary computation (GECCO 2007) 7-11 July 2007, London, England
EditorsHod Lipson
Place of PublicationLondon
PublisherAssociation for Computing Machinery, Inc
ISBN (Print)978-1-59593-697-4
Publication statusPublished - 2007
Eventconference; GECCO 2007, London, England; 2007-07-07; 2007-07-11 -
Duration: 7 Jul 200711 Jul 2007

Conference

Conferenceconference; GECCO 2007, London, England; 2007-07-07; 2007-07-11
Period7/07/0711/07/07
OtherGECCO 2007, London, England

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