Proposition of the Interactive Pareto Iterated Local Search Procedure - Elements and Initial Experiments

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dc.creatorGeiger, Martin Josef
dc.date2008-09-04
dc.date.accessioned2026-07-07T10:00:39Z
dc.date.available2026-07-07T10:00:39Z
dc.descriptionThe article presents an approach to interactively solve multi-objective optimization problems. While the identification of efficient solutions is supported by computational intelligence techniques on the basis of local search, the search is directed by partial preference information obtained from the decision maker. An application of the approach to biobjective portfolio optimization, modeled as the well-known knapsack problem, is reported, and experimental results are reported for benchmark instances taken from the literature. In brief, we obtain encouraging results that show the applicability of the approach to the described problem.
dc.identifierhttps://arxiv.org/abs/0809.0753
dc.identifierhttp://arxiv.org/abs/0809.0753
dc.identifierThe Fourth International Conference on Evolutionary Multi-Criterion Optimization: Late Breaking Papers, Matsushima, Japan, March 2007, pp. 19-23
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/168379
dc.subjectArtificial Intelligence
dc.subjectHuman-Computer Interaction
dc.titleProposition of the Interactive Pareto Iterated Local Search Procedure - Elements and Initial Experiments
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