An approach to instantly use single-objective results for multi-objective evolutionary combinatorial optimization

Authors:
Christian Grimme;Joachim Lepping
Affiliations:
Robotics Research Institute, TU Dortmund University, Dortmund, Germany;INRIA Rhône-Alpes, Grenoble University, Montbonnot-Saint-Martin, France
Venue:
LION'12 Proceedings of the 6th international conference on Learning and Intelligent Optimization
Year:
2012

Citing 5
Cited 0

Minimizing the number of tardy jobs in identical machine scheduling

Proceedings of the 15th annual conference on Computers and industrial engineering
A Spatial Predator-Prey Approach to Multi-objective Optimization: A Preliminary Study

PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)

Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
A fast and elitist multiobjective genetic algorithm: NSGA-II

IEEE Transactions on Evolutionary Computation
Performance assessment of multiobjective optimizers: an analysis and review

IEEE Transactions on Evolutionary Computation

Quantified Score

Hi-index	0.00

Visualization

Abstract

Standard dominance-based multi-objective evolutionary algorithms hardly allow to integrate problem knowledge without redesigning the approach as a whole. We present a flexible alternative approach based on an abstraction from predator-prey interplay. For parallel machine scheduling problems, we find that the combination of problem knowledge principally leads to better trade-off approximations compared to standard class of algorithms, especially NSGA-2. Further, we show that the incremental integration of existing problem knowledge gradually improves the algorithm's performance.