2 citations · 3 across the 4 of their papers we have counts for
4 papers
Challenges of ELA-guided Function Evolution using Genetic Programming
Fu Xing Long, Diederick Vermetten, Anna V. Kononova +4
Within the optimization community, the question of how to generate new optimization problems has been gaining traction in recent years. Within topics such as instance space analysi…
A Parallel Technique for Multi-objective Bayesian Global Optimization: Using a Batch Selection of Probability of Improvement
Kaifeng Yang, Guozhi Dong, Michael Affenzeller
Bayesian global optimization (BGO) is an efficient surrogate-assisted technique for problems involving expensive evaluations. A parallel technique can be used to parallelly evaluat…
Probability Distribution of Hypervolume Improvement in Bi-objective Bayesian Optimization
Hao Wang, Kaifeng Yang, Michael Affenzeller
Hypervolume improvement (HVI) is commonly employed in multi-objective Bayesian optimization algorithms to define acquisition functions due to its Pareto-compliant property. Rather…
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…