4 citations · 7 across the 19 of their papers we have counts for
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Theoretical Analysis of Quality Diversity Algorithms for a Classical Path Planning Problem
Duc-Cuong Dang, Aneta Neumann, Frank Neumann +2
Quality diversity (QD) algorithms have shown to provide sets of high quality solutions for challenging problems in robotics, games, and combinatorial optimisation. So far, theoreti…
Sliding Window Bi-Objective Evolutionary Algorithms for Optimizing Chance-Constrained Monotone Submodular Functions
Xiankun Yan, Aneta Neumann, Frank Neumann
Variants of the GSEMO algorithm using multi-objective formulations have been successfully analyzed and applied to optimize chance-constrained submodular functions. However, due to…
Illuminating the Diversity-Fitness Trade-Off in Black-Box Optimization
Maria Laura Santoni, Elena Raponi, Aneta Neumann +3
In real-world applications, users often favor structurally diverse design choices over one high-quality solution. It is hence important to consider more solutions that decision mak…
Local Optima in Diversity Optimization: Non-trivial Offspring Population is Essential
Denis Antipov, Aneta Neumann, Frank Neumann
The main goal of diversity optimization is to find a diverse set of solutions which satisfy some lower bound on their fitness. Evolutionary algorithms (EAs) are often used for such…
Evolving Reliable Differentiating Constraints for the Chance-constrained Maximum Coverage Problem
Saba Sadeghi Ahouei, Jacob de Nobel, Aneta Neumann +2
Chance-constrained problems involve stochastic components in the constraints which can be violated with a small probability. We investigate the impact of different types of chance…
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…
A Block-Coordinate Descent EMO Algorithm: Theoretical and Empirical Analysis
Benjamin Doerr, Joshua Knowles, Aneta Neumann +1
We consider whether conditions exist under which block-coordinate descent is asymptotically efficient in evolutionary multi-objective optimization, addressing an open problem. Bloc…
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…
Runtime Analysis of Evolutionary Diversity Optimization on the Multi-objective (LeadingOnes, TrailingZeros) Problem
Denis Antipov, Aneta Neumann, Frank Neumann +1
Diversity optimization is the class of optimization problems in which we aim to find a diverse set of good solutions. One of the frequently-used approaches to solve such problems i…
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…