61 citations · 242 across the 48 of their papers we have counts for
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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…
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…
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…
Computing High-Quality Solutions for the Patient Admission Scheduling Problem using Evolutionary Diversity Optimisation
Adel Nikfarjam, Amirhossein Moosavi, Aneta Neumann +1
Diversification in a set of solutions has become a hot research topic in the evolutionary computation community. It has been proven beneficial for optimisation problems in several…
Evolutionary Time-Use Optimization for Improving Children's Health Outcomes
Yue Xie, Aneta Neumann, Ty Stanford +3
How someone allocates their time is important to their health and well-being. In this paper, we show how evolutionary algorithms can be used to promote health and well-being by opt…
Evolutionary Algorithms for Limiting the Effect of Uncertainty for the Knapsack Problem with Stochastic Profits
Aneta Neumann, Yue Xie, Frank Neumann
Evolutionary algorithms have been widely used for a range of stochastic optimization problems in order to address complex real-world optimization problems. We consider the knapsack…
Coevolutionary Pareto Diversity Optimization
Aneta Neumann, Denis Antipov, Frank Neumann
Computing diverse sets of high quality solutions for a given optimization problem has become an important topic in recent years. In this paper, we introduce a coevolutionary Pareto…
Evolutionary Diversity Optimisation for The Traveling Thief Problem
Adel Nikfarjam, Aneta Neumann, Frank Neumann
There has been a growing interest in the evolutionary computation community to compute a diverse set of high-quality solutions for a given optimisation problem. This can provide th…
Defending Active Directory by Combining Neural Network based Dynamic Program and Evolutionary Diversity Optimisation
Diksha Goel, Max Ward, Aneta Neumann +3
Active Directory (AD) is the default security management system for Windows domain networks. We study a Stackelberg game model between one attacker and one defender on an AD attack…
Niching-based Evolutionary Diversity Optimization for the Traveling Salesperson Problem
Anh Viet Do, Mingyu Guo, Aneta Neumann +1
In this work, we consider the problem of finding a set of tours to a traveling salesperson problem (TSP) instance maximizing diversity, while satisfying a given cost constraint. Th…
On the Use of Quality Diversity Algorithms for The Traveling Thief Problem
Adel Nikfarjam, Aneta Neumann, Frank Neumann
In real-world optimisation, it is common to face several sub-problems interacting and forming the main problem. There is an inter-dependency between the sub-problems, making it imp…
Computing Diverse Sets of High Quality TSP Tours by EAX-Based Evolutionary Diversity Optimisation
Adel Nikfarjam, Jakob Bossek, Aneta Neumann +1
Evolutionary algorithms based on edge assembly crossover (EAX) constitute some of the best performing incomplete solvers for the well-known traveling salesperson problem (TSP). Oft…
Entropy-Based Evolutionary Diversity Optimisation for the Traveling Salesperson Problem
Adel Nikfarjam, Jakob Bossek, Aneta Neumann +1
Computing diverse sets of high-quality solutions has gained increasing attention among the evolutionary computation community in recent years. It allows practitioners to choose fro…
Breeding Diverse Packings for the Knapsack Problem by Means of Diversity-Tailored Evolutionary Algorithms
Jakob Bossek, Aneta Neumann, Frank Neumann
In practise, it is often desirable to provide the decision-maker with a rich set of diverse solutions of decent quality instead of just a single solution. In this paper we study ev…
Heuristic Strategies for Solving Complex Interacting Large-Scale Stockpile Blending Problems
Yue Xie, Aneta Neumann, Frank Neumann
The Stockpile blending problem is an important component of mine production scheduling, where stockpiles are used to store and blend raw material. The goal of blending material fro…
Heuristic Strategies for Solving Complex Interacting Stockpile Blending Problem with Chance Constraints
Yue Xie, Aneta Neumann, Frank Neumann
Heuristic algorithms have shown a good ability to solve a variety of optimization problems. Stockpile blending problem as an important component of the mine scheduling problem is a…
Analysis of Evolutionary Diversity Optimisation for Permutation Problems
Anh Viet Do, Mingyu Guo, Aneta Neumann +1
Generating diverse populations of high quality solutions has gained interest as a promising extension to the traditional optimization tasks. This work contributes to this line of r…
Optimising Tours for the Weighted Traveling Salesperson Problem and the Traveling Thief Problem: A Structural Comparison of Solutions
Jakob Bossek, Aneta Neumann, Frank Neumann
The Traveling Salesperson Problem (TSP) is one of the best-known combinatorial optimisation problems. However, many real-world problems are composed of several interacting componen…
Optimizing Monotone Chance-Constrained Submodular Functions Using Evolutionary Multi-Objective Algorithms
Aneta Neumann, Frank Neumann
Many real-world optimization problems can be stated in terms of submodular functions. Furthermore, these real-world problems often involve uncertainties which may lead to the viola…
Specific Single- and Multi-Objective Evolutionary Algorithms for the Chance-Constrained Knapsack Problem
Yue Xie, Aneta Neumann, Frank Neumann
The chance-constrained knapsack problem is a variant of the classical knapsack problem where each item has a weight distribution instead of a deterministic weight. The objective is…