Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Evolving Objects: A General Purpose Evolutionary Computation Library
Selected Papers from the 5th European Conference on Artificial Evolution
The State of the Art of Nurse Rostering
Journal of Scheduling
Reference point based multi-objective optimization using evolutionary algorithms
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Towards estimating nadir objective vector using evolutionary approaches
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Performance scaling of multi-objective evolutionary algorithms
EMO'03 Proceedings of the 2nd international conference on Evolutionary multi-criterion optimization
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Objective reduction using a feature selection technique
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Multiobjective robustness for portfolio optimization in volatile environments
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Objective reduction in evolutionary multiobjective optimization: Theory and applications
Evolutionary Computation
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Study of preference relations in many-objective optimization
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Using a distance metric to guide PSO algorithms for many-objective optimization
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
A Distance Metric for Evolutionary Many-Objective Optimization Algorithms Using User-Preferences
AI '09 Proceedings of the 22nd Australasian Joint Conference on Advances in Artificial Intelligence
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part II
IEEE Transactions on Evolutionary Computation - Special issue on preference-based multiobjective evolutionary algorithms
Alleviate the hypervolume degeneration problem of NSGA-II
ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part II
Refined ranking relations for multi objective optimization andapplication to P-ACO
Proceedings of the 15th annual conference on Genetic and evolutionary computation
Borg: An auto-adaptive many-objective evolutionary computing framework
Evolutionary Computation
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In most real world optimization problems several optimization goals have to be considered in parallel. For this reason, there has been a growing interest in Multi-Objective Optimization (MOO) in the past years. Several alternative approaches have been proposed to cope with the occurring problems, e.g. how to compare and rank the different elements. The available techniques produce very good results, but they have mainly been studied for problems of "low dimension", i.e. with less than 10 optimization objectives. In this paper we study MOO for high dimensional spaces. We first review existing techniques and discuss them in our context. The pros and cons are pointed out. A new relation called ∈-Preferred is presented that extends existing approaches and clearly outperforms these for high dimensions. Experimental results are presented for a very complex industrial scheduling problem, i.e. a utilization planning problem for a hospital. This problem is also well known as nurse rostering, and in our application has more than 20 optimization targets. It is solved using an evolutionary approach. The new algorithms based on relation ∈-Preferred do not only yield better results regarding quality, but also enhances the robustness significantly.