Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
A comprehensive survey of fitness approximation in evolutionary computation
Soft Computing - A Fusion of Foundations, Methodologies and Applications
A support vector method for multivariate performance measures
ICML '05 Proceedings of the 22nd international conference on Machine learning
Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
Evolutionary Computation
Estimating the Support of a High-Dimensional Distribution
Neural Computation
Covariance Matrix Adaptation for Multi-objective Optimization
Evolutionary Computation
Meta-Modeling in Multiobjective Optimization
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
A mono surrogate for multiobjective optimization
Proceedings of the 12th annual conference on Genetic and evolutionary computation
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Local meta-models for ASM-MOMA
ICIC'11 Proceedings of the 7th international conference on Intelligent Computing: bio-inspired computing and applications
Local meta-models for ASM-MOMA
ICIC'11 Proceedings of the 7th international conference on Advanced Intelligent Computing
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Mainstream surrogate approaches for multi-objective problems build one approximation for each objective. Mono-surrogate approaches instead aim at characterizing the Pareto front with a single model. Such an approach has been recently introduced using a mixture of regression Support VectorMachine (SVM) to clamp the current Pareto front to a single value, and one-class SVM to ensure that all dominated points will be mapped on one side of this value. A new mono-surrogate EMO approach is introduced here, relaxing the previous approach and modelling Pareto dominance within the rank-SVM framework. The resulting surrogate model is then used as a filter for offspring generation in standard Evolutionary Multi-Objective Algorithms, and is comparatively validated on a set of benchmark problems.