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
Genetic Algorithms for Multiobjective Optimization: FormulationDiscussion and Generalization
Proceedings of the 5th International Conference on Genetic Algorithms
Incorporation Of Fuzzy Preferences Into Evolutionary Multiobjective Optimization
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Completely Derandomized Self-Adaptation in Evolution Strategies
Evolutionary Computation
Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
Evolutionary Computation
Reference point based multi-objective optimization using evolutionary algorithms
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Integrating user preferences with particle swarms for multi-objective optimization
Proceedings of the 10th annual conference on Genetic and evolutionary computation
SPAM: Set Preference Algorithm for Multiobjective Optimization
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
Multiobjective Optimization: Interactive and Evolutionary Approaches
Multiobjective Optimization: Interactive and Evolutionary Approaches
Interactive Multiobjective Optimization Using a Set of Additive Value Functions
Multiobjective Optimization
Dominance-Based Rough Set Approach to Interactive Multiobjective Optimization
Multiobjective Optimization
Consideration of Partial User Preferences in Evolutionary Multiobjective Optimization
Multiobjective Optimization
Interactive Multiobjective Evolutionary Algorithms
Multiobjective Optimization
Theory of the hypervolume indicator: optimal μ-distributions and the choice of the reference point
Proceedings of the tenth ACM SIGEVO workshop on Foundations of genetic algorithms
An Evolutionary Metaheuristic for Approximating Preference-Nondominated Solutions
INFORMS Journal on Computing
On the complexity of computing the hypervolume indicator
IEEE Transactions on Evolutionary Computation
Proceedings of the 4th international conference on Evolutionary multi-criterion optimization
EMO'07 Proceedings of the 4th international conference on Evolutionary multi-criterion optimization
Proceedings of the 4th international conference on Evolutionary multi-criterion optimization
EMO'07 Proceedings of the 4th international conference on Evolutionary multi-criterion optimization
Robust multi-objective optimization in high dimensional spaces
EMO'07 Proceedings of the 4th international conference on Evolutionary multi-criterion optimization
Pareto-, aggregation-, and indicator-based methods in many-objective optimization
EMO'07 Proceedings of the 4th international conference on Evolutionary multi-criterion optimization
EMO'07 Proceedings of the 4th international conference on Evolutionary multi-criterion optimization
EMO'07 Proceedings of the 4th international conference on Evolutionary multi-criterion optimization
Evolutionary multi-objective optimization algorithm with preference for mechanical design
ICMLC'05 Proceedings of the 4th international conference on Advances in Machine Learning and Cybernetics
An EMO algorithm using the hypervolume measure as selection criterion
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
Preferences and their application in evolutionary multiobjectiveoptimization
IEEE Transactions on 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
An Investigation on Preference Order Ranking Scheme for Multiobjective Evolutionary Optimization
IEEE Transactions on Evolutionary Computation
A new multi-objective evolutionary algorithm based on a performance assessment indicator
Proceedings of the 14th annual conference on Genetic and evolutionary computation
Computational Statistics & Data Analysis
Hi-index | 0.00 |
In this paper, a concept for efficiently approximating the practically relevant regions of the Pareto front (PF) is introduced. Instead of the original objectives, desirability functions (DFs) of the objectives are optimized, which express the preferences of the decision maker. The original problem formulation and the optimization algorithm do not have to be modified. DFs map an objective to the domain [0, 1] and nonlinearly increase with better objective quality. By means of this mapping, values of different objectives and units become comparable. A biased distribution of the solutions in the PF approximation based on different scalings of the objectives is prevented. Thus, we propose the integration of DFs into the S-metric selection evolutionary multiobjective algorithm. The transformation ensures the meaning of the hypervolumes internally computed. Furthermore, it is shown that the reference point for the hypervolume calculation can be set intuitively. The approach is analyzed using standard test problems. Moreover, a practical validation by means of the optimization of a turning process is performed.