8 citations · 20 across the 14 of their papers we have counts for
21 papers
Sampling-based Pareto Optimization for Chance-constrained Monotone Submodular Problems
Xiankun Yan, Aneta Neumann, Frank Neumann
Recently surrogate functions based on the tail inequalities were developed to evaluate the chance constraints in the context of evolutionary computation and several Pareto optimiza…
Analysis of Evolutionary Diversity Optimisation for the Maximum Matching Problem
Jonathan Gadea Harder, Aneta Neumann, Frank Neumann
This paper explores the enhancement of solution diversity in evolutionary algorithms (EAs) for the maximum matching problem, concentrating on complete bipartite graphs and paths. W…
Multi-Objective Evolutionary Algorithms with Sliding Window Selection for the Dynamic Chance-Constrained Knapsack Problem
Kokila Kasuni Perera, Aneta Neumann
Evolutionary algorithms are particularly effective for optimisation problems with dynamic and stochastic components. We propose multi-objective evolutionary approaches for the knap…
Using 3-Objective Evolutionary Algorithms for the Dynamic Chance Constrained Knapsack Problem
Ishara Hewa Pathiranage, Frank Neumann, Denis Antipov +1
Real-world optimization problems often involve stochastic and dynamic components. Evolutionary algorithms are particularly effective in these scenarios, as they can easily adapt to…
Evolutionary Multi-Objective Diversity Optimization
Anh Viet Do, Mingyu Guo, Aneta Neumann +1
Creating diverse sets of high quality solutions has become an important problem in recent years. Previous works on diverse solutions problems consider solutions' objective quality…
Diverse Approximations for Monotone Submodular Maximization Problems with a Matroid Constraint
Anh Viet Do, Mingyu Guo, Aneta Neumann +1
Finding diverse solutions to optimization problems has been of practical interest for several decades, and recently enjoyed increasing attention in research. While submodular optim…