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The hypervolume indicator: Computational problems and algorithms
The hypervolume indicator is one of the most used set-quality indicators for the assessment
of stochastic multiobjective optimizers, as well as for selection in evolutionary multiobjective …
of stochastic multiobjective optimizers, as well as for selection in evolutionary multiobjective …
Differentiable expected hypervolume improvement for parallel multi-objective Bayesian optimization
In many real-world scenarios, decision makers seek to efficiently optimize multiple
competing objectives in a sample-efficient fashion. Multi-objective Bayesian optimization …
competing objectives in a sample-efficient fashion. Multi-objective Bayesian optimization …
A survey on the hypervolume indicator in evolutionary multiobjective optimization
Hypervolume is widely used as a performance indicator in the field of evolutionary
multiobjective optimization (EMO). It is used not only for performance evaluation of EMO …
multiobjective optimization (EMO). It is used not only for performance evaluation of EMO …
A first mathematical runtime analysis of the Non-Dominated Sorting Genetic Algorithm II (NSGA-II)
The non-dominated sorting genetic algorithm II (NSGA-II) is the most intensively used multi-
objective evolutionary algorithm (MOEA) in real-world applications. However, in contrast to …
objective evolutionary algorithm (MOEA) in real-world applications. However, in contrast to …
A survey on recent progress in the theory of evolutionary algorithms for discrete optimization
The theory of evolutionary computation for discrete search spaces has made significant
progress since the early 2010s. This survey summarizes some of the most important recent …
progress since the early 2010s. This survey summarizes some of the most important recent …
Stochastic population update can provably be helpful in multi-objective evolutionary algorithms
Evolutionary algorithms (EAs) have been widely and successfully applied to solve multi-
objective optimization problems, due to their nature of population-based search. Population …
objective optimization problems, due to their nature of population-based search. Population …
Better running time of the non-dominated sorting genetic algorithm II (NSGA-II) by using stochastic tournament selection
Evolutionary algorithms (EAs) have been widely used to solve multi-objective optimization
problems, and have become the most popular tool. However, the theoretical foundation of …
problems, and have become the most popular tool. However, the theoretical foundation of …
[HTML][HTML] Mathematical runtime analysis for the non-dominated sorting genetic algorithm II (NSGA-II)
The non-dominated sorting genetic algorithm II (NSGA-II) is the most intensively used multi-
objective evolutionary algorithm (MOEA) in real-world applications. However, in contrast to …
objective evolutionary algorithm (MOEA) in real-world applications. However, in contrast to …
The hypervolume indicator: Problems and algorithms
The hypervolume indicator is one of the most used set-quality indicators for the assessment
of stochastic multiobjective optimizers, as well as for selection in evolutionary multiobjective …
of stochastic multiobjective optimizers, as well as for selection in evolutionary multiobjective …
Pareto optimization for subset selection with dynamic cost constraints
We consider the subset selection problem for function f with constraint bound B that changes
over time. Within the area of submodular optimization, various greedy approaches are …
over time. Within the area of submodular optimization, various greedy approaches are …