61 citations · 242 across the 48 of their papers we have counts for
12 papers · 1 filter
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…
Rigorous Runtime Analysis of Diversity Optimization with GSEMO on OneMinMax
Denis Antipov, Aneta Neumann, Frank Neumann
The evolutionary diversity optimization aims at finding a diverse set of solutions which satisfy some constraint on their fitness. In the context of multi-objective optimization th…
Rigorous Runtime Analysis of MOEA/D for Solving Multi-Objective Minimum Weight Base Problems
Anh Viet Do, Aneta Neumann, Frank Neumann +1
We study the multi-objective minimum weight base problem, an abstraction of classical NP-hard combinatorial problems such as the multi-objective minimum spanning tree problem. We p…
On the Impact of Operators and Populations within Evolutionary Algorithms for the Dynamic Weighted Traveling Salesperson Problem
Jakob Bossek, Aneta Neumann, Frank Neumann
Evolutionary algorithms have been shown to obtain good solutions for complex optimization problems in static and dynamic environments. It is important to understand the behaviour o…
Analysis of the (1+1) EA on LeadingOnes with Constraints
Tobias Friedrich, Timo Kötzing, Aneta Neumann +2
Understanding how evolutionary algorithms perform on constrained problems has gained increasing attention in recent years. In this paper, we study how evolutionary algorithms optim…
Improving Confidence in Evolutionary Mine Scheduling via Uncertainty Discounting
Michael Stimson, William Reid, Aneta Neumann +2
Mine planning is a complex task that involves many uncertainties. During early stage feasibility, available mineral resources can only be estimated based on limited sampling of ore…
Evolving Reinforcement Learning Environment to Minimize Learner's Achievable Reward: An Application on Hardening Active Directory Systems
Diksha Goel, Aneta Neumann, Frank Neumann +2
We study a Stackelberg game between one attacker and one defender in a configurable environment. The defender picks a specific environment configuration. The attacker observes the…
Fixed Parameter Multi-Objective Evolutionary Algorithms for the W-Separator Problem
Samuel Baguley, Tobias Friedrich, Aneta Neumann +3
Parameterized analysis provides powerful mechanisms for obtaining fine-grained insights into different types of algorithms. In this work, we combine this field with evolutionary al…
Evolutionary Multi-Objective Algorithms for the Knapsack Problems with Stochastic Profits
Kokila Perera, Aneta Neumann, Frank Neumann
Evolutionary multi-objective algorithms have been widely shown to be successful when utilized for a variety of stochastic combinatorial optimization problems. Chance constrained op…
Towards Self-adaptive Mutation in Evolutionary Multi-Objective Algorithms
Furong Ye, Frank Neumann, Jacob de Nobel +2
Parameter control has succeeded in accelerating the convergence process of evolutionary algorithms. While empirical and theoretical studies have shed light on the behavior of algor…
Benchmarking Algorithms for Submodular Optimization Problems Using IOHProfiler
Frank Neumann, Aneta Neumann, Chao Qian +7
Submodular functions play a key role in the area of optimization as they allow to model many real-world problems that face diminishing returns. Evolutionary algorithms have been sh…
Limited Query Graph Connectivity Test
Mingyu Guo, Jialiang Li, Aneta Neumann +2
We propose a combinatorial optimisation model called Limited Query Graph Connectivity Test. We consider a graph whose edges have two possible states (On/Off). The edges' states are…