4 citations · 7 across the 19 of their papers we have counts for
15 papers · 1 filter
Insights from Multi-tasking the EAX Algorithm for the Travelling Salesperson Problem
Liam Wigney, Aneta Neumann, Yew-Soon Ong +1
Evolutionary multitasking allows several related problems to be solved in a single run of an algorithm. In this paper, we investigate integrating evolutionary multitasking with Edg…
On the Use of Survival Selection Methods for Evolutionary Diversity Optimisation
Adel Nikfarjam, Jakob Bossek, Aneta Neumann +1
Generating a diverse set of high quality solutions for an optimisation problem has been studied extensively in recent years by the evolutionary computation community. A paradigm th…
Trust Region-Based Bayesian Optimisation to Discover Diverse Solutions
Kokila Kasuni Perera, Frank Neumann, Aneta Neumann
Bayesian optimisation (BO) is a surrogate-based optimisation technique that efficiently solves expensive black-box functions with small evaluation budgets. Recent studies consider…
Quality Diversity Genetic Programming for Learning Scheduling Heuristics
Meng Xu, Frank Neumann, Aneta Neumann +1
Real-world optimization often demands diverse, high-quality solutions. Quality-Diversity (QD) optimization is a multifaceted approach in evolutionary algorithms that aims to genera…
Weighted-Scenario Optimisation for the Chance Constrained Travelling Thief Problem
Thilina Pathirage Don, Aneta Neumann, Frank Neumann
The chance constrained travelling thief problem (chance constrained TTP) has been introduced as a stochastic variation of the classical travelling thief problem (TTP) in an attempt…
Feature-based Evolutionary Diversity Optimization of Discriminating Instances for Chance-constrained Optimization Problems
Saba Sadeghi Ahouei, Denis Antipov, Aneta Neumann +1
Algorithm selection is crucial in the field of optimization, as no single algorithm performs perfectly across all types of optimization problems. Finding the best algorithm among a…
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…
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…
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…
Evolutionary Algorithms for the Chance-Constrained Knapsack Problem
Yue Xie, Oscar Harper, Hirad Assimi +2
Evolutionary algorithms have been applied to a wide range of stochastic problems. Motivated by real-world problems where constraint violations have disruptive effects, this paper c…