11 citations · 24 across the 5 of their papers we have counts for
5 papers
Efficient Computation of Expected Hypervolume Improvement Using Box Decomposition Algorithms
Kaifeng Yang, Michael Emmerich, André Deutz +1
In the field of multi-objective optimization algorithms, multi-objective Bayesian Global Optimization (MOBGO) is an important branch, in addition to evolutionary multi-objective op…
Hyper-Parameter Sweep on AlphaZero General
Hui Wang, Michael Emmerich, Mike Preuss +1
Since AlphaGo and AlphaGo Zero have achieved breakground successes in the game of Go, the programs have been generalized to solve other tasks. Subsequently, AlphaZero was developed…
An Ontology of Preference-Based Multiobjective Metaheuristics
Longmei Li, Iryna Yevseyeva, Vitor Basto-Fernandes +3
User preference integration is of great importance in multi-objective optimization, in particular in many objective optimization. Preferences have long been considered in tradition…
Multiobjective Optimization of Classifiers by Means of 3-D Convex Hull Based Evolutionary Algorithm
Jiaqi Zhao, Vitor Basto Fernandes, Licheng Jiao +5
Finding a good classifier is a multiobjective optimization problem with different error rates and the costs to be minimized. The receiver operating characteristic is widely used in…
Faster Computation of Expected Hypervolume Improvement
Iris Hupkens, Michael Emmerich, André Deutz
The expected improvement algorithm (or efficient global optimization) aims for global continuous optimization with a limited budget of black-box function evaluations. It is based o…