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2017 | OriginalPaper | Buchkapitel

Towards Standardized and Seamless Integration of Expert Knowledge into Multi-objective Evolutionary Optimization Algorithms

verfasst von : Magdalena A. K. Lang, Christian Grimme

Erschienen in: Evolutionary Multi-Criterion Optimization

Verlag: Springer International Publishing

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Abstract

Evolutionary algorithms allow for solving a wide range of multi-objective optimization problems. Nevertheless for complex practical problems, including domain knowledge is imperative to achieve good results. In many domains, single-objective expert knowledge is available, but its integration into modern multi-objective evolutionary algorithms (MOEAs) is often not trivial and infeasible for practitioners. In addition to the need of modifying algorithm architectures, the challenge of combining single-objective knowledge to multi-objective rules arises. This contribution takes a step towards a multi-objective optimization framework with defined interfaces for expert knowledge integration. Therefore, multi-objective mutation and local search operators are integrated into the two MOEAs MOEA/D and R-NSGAII. Results from experiments on exemplary machine scheduling problems prove the potential of such a concept and motivate further research in this direction.

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Vorheriges Kapitel Multi-objective Adaptation of a Parameterized GVGAI Agent Towards Several Games

Nächstes Kapitel Empirical Investigations of Reference Point Based Methods When Facing a Massively Large Number of Objectives: First Results

Note, however, that we focus on the more general context of applicability in MOEAs. Thus, the proposed methodology is not restricted to specific applications but strive for identifying additional integration points of expertise.

This configuration was applied without fine tuning to demonstrate the methodology. Future rigorous investigation may consider systematically generated configurations.

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Titel: Towards Standardized and Seamless Integration of Expert Knowledge into Multi-objective Evolutionary Optimization Algorithms
verfasst von: Magdalena A. K. Lang
Christian Grimme
Verlag: Springer International Publishing
Buch: Evolutionary Multi-Criterion Optimization
Print ISBN: 978-3-319-54156-3

Electronic ISBN: 978-3-319-54157-0

Copyright-Jahr: 2017
DOI: https://doi.org/10.1007/978-3-319-54157-0_26

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