Robust recognition using eigenimages
Computer Vision and Image Understanding - Special issue on robusst statistical techniques in image understanding
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Evolutionary Algorithms for Solving Multi-Objective Problems
Evolutionary Algorithms for Solving Multi-Objective Problems
Multiple Objective Optimization with Vector Evaluated Genetic Algorithms
Proceedings of the 1st International Conference on Genetic Algorithms
Genetic Algorithms for Multiobjective Optimization: FormulationDiscussion and Generalization
Proceedings of the 5th International Conference on Genetic Algorithms
Multiobjective Optimization Using Evolutionary Algorithms - A Comparative Case Study
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Multiobjective evolutionary algorithms: classifications, analyses, and new innovations
Multiobjective evolutionary algorithms: classifications, analyses, and new innovations
Robust Handling of Multiple Multi-Objective Optimisations
E-SCIENCE '06 Proceedings of the Second IEEE International Conference on e-Science and Grid Computing
An EMO algorithm using the hypervolume measure as selection criterion
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
Recombination of similar parents in EMO algorithms
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
| Hi-index | 0.00 |
Considering external parameters during any evaluation leads to an optimization problem which has to handle several concurrent multi objective problems at once. This novel challenge, the Multiple Multi Objective Problem M-MOP, is defined and analyzed. Guidelines and metrics for the development of M-MOP optimizers are generated and exemplary demonstrated at an extended version of Deb's NSGA-II algorithm. The relationship to the classical MOPs is highlighted and the usage of performance metrics for the M-MOP is discussed. Due to the increased number of dimensions the M-MOP represents a complex optimization task that should be settled in the optimization community.