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

  • Magdalena A. K. Lang
  • Christian GrimmeEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10173)


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.


Multi-objective evolutionary algorithm Expert knowledge integration MOEA/D R-NSGAII Scheduling 


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.RWTH Aachen UniversityAachenGermany
  2. 2.Westfälische Wilhelms-Universität MünsterMünsterGermany

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