Publications (174)
The Packing While Traveling Problem
Sergey Polyakovskiy, Frank Neumann
This paper introduces the Packing While Traveling problem as a new non-linear knapsack problem. Given are a set of cities that have a set of items of distinct profits and weights a…
The Compact Genetic Algorithm Struggles on Cliff Functions
Frank Neumann, Dirk Sudholt, Carsten Witt
The compact genetic algorithm (cGA) is an non-elitist estimation of distribution algorithm which has shown to be able to deal with difficult multimodal fitness landscapes that are…
André spectral sequences for Baues-Wirsching cohomology of categories
Imma Gálvez-Carrillo, Frank Neumann, Andrew Tonks
We construct spectral sequences in the framework of Baues-Wirsching cohomology and homology for functors between small categories and analyze particular cases including Grothendiec…
Parameterized Runtime Analyses of Evolutionary Algorithms for the Euclidean Traveling Salesperson Problem
Andrew M. Sutton, Frank Neumann
Parameterized runtime analysis seeks to understand the influence of problem structure on algorithmic runtime. In this paper, we contribute to the theoretical understanding of evolu…
Evolutionary Image Transition Based on Theoretical Insights of Random Processes
Aneta Neumann, Bradley Alexander, Frank Neumann
Evolutionary algorithms have been widely studied from a theoretical perspective. In particular, the area of runtime analysis has contributed significantly to a theoretical understa…
Parameterized Analysis of Multi-objective Evolutionary Algorithms and the Weighted Vertex Cover Problem
Mojgan Pourhassan, Feng Shi, Frank Neumann
A rigorous runtime analysis of evolutionary multi-objective optimization for the classical vertex cover problem in the context of parameterized complexity analysis has been present…
On the Behaviour of Differential Evolution for Problems with Dynamic Linear Constraints
Maryam Hasani-Shoreh, MarÃa-Yaneli Ameca-Alducin, Wilson Blaikie +2
Evolutionary algorithms have been widely applied for solving dynamic constrained optimization problems (DCOPs) as a common area of research in evolutionary optimization. Current be…
Evolutionary Algorithms and Dynamic Programming
Benjamin Doerr, Anton Eremeev, Frank Neumann +2
Recently, it has been proven that evolutionary algorithms produce good results for a wide range of combinatorial optimization problems. Some of the considered problems are tackled…
A Comparison of Constraint Handling Techniques for Dynamic Constrained Optimization Problems
Maria-Yaneli Ameca-Alducin, Maryam Hasani-Shoreh, Wilson Blaikie +2
Dynamic constrained optimization problems (DCOPs) have gained researchers attention in recent years because a vast majority of real world problems change over time. There are studi…
A Study of Fitness Gains in Evolving Finite State Machines
Gabor Zoltai, Yue Xie, Frank Neumann
Among the wide variety of evolutionary computing models, Finite State Machines (FSMs) have several attractions for fundamental research. They are easy to understand in concept and…
On the Use of Diversity Mechanisms in Dynamic Constrained Continuous Optimization
Maryam Hasani-Shoreh, Frank Neumann
Population diversity plays a key role in evolutionary algorithms that enables global exploration and avoids premature convergence. This is especially more crucial in dynamic optimi…
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…
Exact Approaches for the Travelling Thief Problem
Junhua Wu, Markus Wagner, Sergey Polyakovskiy +1
Many evolutionary and constructive heuristic approaches have been introduced in order to solve the Traveling Thief Problem (TTP). However, the accuracy of such approaches is unknow…
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…
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…
Greedy Maximization of Functions with Bounded Curvature under Partition Matroid Constraints
Tobias Friedrich, Andreas Göbel, Frank Neumann +2
We investigate the performance of a deterministic GREEDY algorithm for the problem of maximizing functions under a partition matroid constraint. We consider non-monotone submodular…
Block-Bench: A Framework for Controllable and Transparent Discrete Optimization Benchmarking
Furong Ye, Frank Neumann, Thomas Bäck +1
We present a novel approach for constructing discrete optimization benchmarks that enables fine-grained control over problem properties, and such benchmarks can facilitate analyzin…
On the Use of Iterative Problem Solving for the Traveling Salesperson Problem with Changing Time Window Constraints
Hy Nguyen, Thanh Nguyen Pham, Helen Yuliana Angmalisang +2
In many real-world settings, problem instances that need to be solved are quite similar, and knowledge from previous optimization runs can potentially be utilized. We explore this…
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…
Robust Fitting in Computer Vision: Easy or Hard?
Tat-Jun Chin, Zhipeng Cai, Frank Neumann
Robust model fitting plays a vital role in computer vision, and research into algorithms for robust fitting continues to be active. Arguably the most popular paradigm for robust fi…
Analysis of Baseline Evolutionary Algorithms for the Packing While Travelling Problem
Vahid Roostapour, Mojgan Pourhassan, Frank Neumann
The performance of base-line Evolutionary Algorithms (EAs) on combinatorial problems has been studied rigorously. From the theoretical viewpoint, the literature extensively investi…
A Parameterized Complexity Analysis of Bi-level Optimisation with Evolutionary Algorithms
Dogan Corus, Per Kristian Lehre, Frank Neumann +1
Bi-level optimisation problems have gained increasing interest in the field of combinatorial optimisation in recent years. With this paper, we start the runtime analysis of evoluti…
Neural Networks in Evolutionary Dynamic Constrained Optimization: Computational Cost and Benefits
Maryam Hasani-Shoreh, Renato Hermoza Aragonés, Frank Neumann
Neural networks (NN) have been recently applied together with evolutionary algorithms (EAs) to solve dynamic optimization problems. The applied NN estimates the position of the nex…
Moduli Stacks of Vector Bundles and Frobenius Morphisms
Frank Neumann, Ulrich Stuhler
We describe the action of the different Frobenius morphisms on the cohomology ring of the moduli stack of algebraic vector bundles of fixed rank and determinant on an algebraic cur…
Pareto Optimization for Subset Selection with Dynamic Partition Matroid Constraints
Anh Viet Do, Frank Neumann
In this study, we consider the subset selection problems with submodular or monotone discrete objective functions under partition matroid constraints where the thresholds are dynam…
Packing While Traveling: Mixed Integer Programming for a Class of Nonlinear Knapsack Problems
Sergey Polyakovskiy, Frank Neumann
Packing and vehicle routing problems play an important role in the area of supply chain management. In this paper, we introduce a non-linear knapsack problem that occurs when packi…
Computing Diverse Sets of Solutions for Monotone Submodular Optimisation Problems
Aneta Neumann, Jakob Bossek, Frank Neumann
Submodular functions allow to model many real-world optimisation problems. This paper introduces approaches for computing diverse sets of high quality solutions for submodular opti…
Evolutionary Image Transition and Painting Using Random Walks
Aneta Neumann, Bradley Alexander, Frank Neumann
We present a study demonstrating how random walk algorithms can be used for evolutionary image transition. We design different mutation operators based on uniform and biased random…
Runtime Performance of Evolutionary Algorithms for the Chance-constrained Makespan Scheduling Problem
Feng Shi, Daoyu Huang, Xiankun Yan +1
The Makespan Scheduling problem is an extensively studied NP-hard problem, and its simplest version looks for an allocation approach for a set of jobs with deterministic processing…
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…
Quasi-random Agents for Image Transition and Animation
Aneta Neumann, Frank Neumann, Tobias Friedrich
Quasi-random walks show similar features as standard random walks, but with much less randomness. We utilize this established model from discrete mathematics and show how agents ca…
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…
Design and Analysis of Diversity-Based Parent Selection Schemes for Speeding Up Evolutionary Multi-objective Optimisation
Edgar Covantes Osuna, Wanru Gao, Frank Neumann +1
Parent selection in evolutionary algorithms for multi-objective optimisation is usually performed by dominance mechanisms or indicator functions that prefer non-dominated points. W…
Effective Traveling for Metric Instances of the Traveling Thief Problem
Jan Eube, Kelin Luo, Aneta Neumann +2
The Traveling Thief Problem (TTP) is a multi-component optimization problem that captures the interplay between routing and packing decisions by combining the classical Traveling S…
Evolutionary Bi-objective Optimization for the Dynamic Chance-Constrained Knapsack Problem Based on Tail Bound Objectives
Hirad Assimi, Oscar Harper, Yue Xie +2
Real-world combinatorial optimization problems are often stochastic and dynamic. Therefore, it is essential to make optimal and reliable decisions with a holistic approach. In this…
Practical Fixed-Parameter Algorithms for Defending Active Directory Style Attack Graphs
Mingyu Guo, Jialiang Li, Aneta Neumann +2
Active Directory is the default security management system for Windows domain networks. We study the shortest path edge interdiction problem for defending Active Directory style at…
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…
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…
A Practical Maximum Clique Algorithm for Matching with Pairwise Constraints
Ãlvaro Parra, Tat-Jun Chin, Frank Neumann +2
A popular paradigm for 3D point cloud registration is by extracting 3D keypoint correspondences, then estimating the registration function from the correspondences using a robust a…
Runtime Analysis of Evolutionary Algorithms with Biased Mutation for the Multi-Objective Minimum Spanning Tree Problem
Vahid Roostapour, Jakob Bossek, Frank Neumann
Evolutionary algorithms (EAs) are general-purpose problem solvers that usually perform an unbiased search. This is reasonable and desirable in a black-box scenario. For combinatori…
A Feature-Based Prediction Model of Algorithm Selection for Constrained Continuous Optimisation
Shayan Poursoltan, Frank Neumann
With this paper, we contribute to the growing research area of feature-based analysis of bio-inspired computing. In this research area, problem instances are classified according t…
On actions of Frobenius morphisms for moduli stacks of principal bundles over algebraic curves
Abel Castorena, Frank Neumann
We study the various arithmetic and geometric Frobenius morphisms on the moduli stack of principal bundles over a smooth projective algebraic curve and determine explicitly their a…
Using Neural Networks and Diversifying Differential Evolution for Dynamic Optimisation
Maryam Hasani Shoreh, Renato Hermoza Aragonés, Frank Neumann
Dynamic optimisation occurs in a variety of real-world problems. To tackle these problems, evolutionary algorithms have been extensively used due to their effectiveness and minimum…
Evolutionary computation for multicomponent problems: opportunities and future directions
Mohammad Reza Bonyadi, Zbigniew Michalewicz, Frank Neumann +1
Over the past 30 years many researchers in the field of evolutionary computation have put a lot of effort to introduce various approaches for solving hard problems. Most of these p…
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…
Cohomology of Moduli Stacks of Principal -Bundles Over Nodal Algebraic Curves
Abel Castorena, Frank Neumann
We study moduli stacks of principal -bundles over nodal complex algebraic curves and determine their rational cohomology algebras in terms of Chern classes.
Optimization of Chance-Constrained Submodular Functions
Benjamin Doerr, Carola Doerr, Aneta Neumann +2
Submodular optimization plays a key role in many real-world problems. In many real-world scenarios, it is also necessary to handle uncertainty, and potentially disruptive events th…
Maximizing Submodular or Monotone Functions under Partition Matroid Constraints by Multi-objective Evolutionary Algorithms
Anh Viet Do, Frank Neumann
Many important problems can be regarded as maximizing submodular functions under some constraints. A simple multi-objective evolutionary algorithm called GSEMO has been shown to ac…
Solving Hard Control Problems in Voting Systems via Integer Programming
Sergey Polyakovskiy, Rudolf Berghammer, Frank Neumann
Voting problems are central in the area of social choice. In this article, we investigate various voting systems and types of control of elections. We present integer linear progra…
Quality Diversity for Reliable Data Driven Time-Use Optimization
Aneta Neumann, Ty Stanford, Dorothea Dumuid +1
The daily allocation of the finite 24-hour time budget is strongly associated with physical, mental, and cognitive health. While predictive models can estimate the relationship bet…
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…
Exact Counting and Sampling of Optima for the Knapsack Problem
Jakob Bossek, Aneta Neumann, Frank Neumann
Computing sets of high quality solutions has gained increasing interest in recent years. In this paper, we investigate how to obtain sets of optimal solutions for the classical kna…
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…
The Node Weight Dependent Traveling Salesperson Problem: Approximation Algorithms and Randomized Search Heuristics
Jakob Bossek, Katrin Casel, Pascal Kerschke +1
Several important optimization problems in the area of vehicle routing can be seen as a variant of the classical Traveling Salesperson Problem (TSP). In the area of evolutionary co…
The Evolutionary Process of Image Transition in Conjunction with Box and Strip Mutation
Aneta Neumann, Bradley Alexander, Frank Neumann
Evolutionary algorithms have been used in many ways to generate digital art. We study how evolutionary processes are used for evolutionary art and present a new approach to the tra…
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…
Atiyah sequences and connections on principal bundles over differentiable stacks
Indranil Biswas, Saikat Chatterjee, Praphulla Koushik +1
We construct and study general connections on Lie groupoids and differentiable stacks as well as on principal bundles over them using Atiyah sequences associated to transversal tan…
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…
Optimizing Chance-Constrained Submodular Problems with Variable Uncertainties
Xiankun Yan, Anh Viet Do, Feng Shi +2
Chance constraints are frequently used to limit the probability of constraint violations in real-world optimization problems where the constraints involve stochastic components. We…
Connections on Lie groupoids and Chern-Weil theory
Indranil Biswas, Saikat Chatterjee, Praphulla Koushik +1
Let be a Lie groupoid equipped with a connection, given by a smooth distribution transversal to the fibers of th…
Evolutionary Computation plus Dynamic Programming for the Bi-Objective Travelling Thief Problem
Junhua Wu, Sergey Polyakovskiy, Markus Wagner +1
This research proposes a novel indicator-based hybrid evolutionary approach that combines approximate and exact algorithms. We apply it to a new bi-criteria formulation of the trav…
Evolutionary Algorithms and Multi-Objective Minimum Spanning Trees with Limited Distinct Weight Values
Narges Tavassoli Kejani, Andrew M. Sutton, Frank Neumann
Evolutionary algorithms have been used for a wide range of multi-objective combinatorial optimization problems. Despite practical success, theoretical results on the runtime of evo…
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…
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…
Runtime Analyses of NSGA-III on Many-Objective Problems
Andre Opris, Duc-Cuong Dang, Frank Neumann +1
NSGA-II and NSGA-III are two of the most popular evolutionary multi-objective algorithms used in practice. While NSGA-II is used for few objectives such as 2 and 3, NSGA-III is des…
Etale homotopy types of moduli stacks of algebraic curves with symmetries
Paola Frediani, Frank Neumann
Using the machinery of etale homotopy theory a' la Artin-Mazur we determine the etale homotopy types of moduli stacks over $\bar{\Q}$ parametrizing families of algebraic curves of…
Greedy Approaches for Packing While Travelling with Deterministic and Stochastic Constraints
Thilina Pathirage Don, Aneta Neumann, Frank Neumann
The travelling thief problem (TTP) is a well-known multi-component optimisation problem that captures the interdependence between two components: the tour across cities and the pac…
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…
Parameterized Complexity Analysis of Randomized Search Heuristics
Frank Neumann, Andrew M. Sutton
This chapter compiles a number of results that apply the theory of parameterized algorithmics to the running-time analysis of randomized search heuristics such as evolutionary algo…
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…
Evolutionary Image Composition Using Feature Covariance Matrices
Aneta Neumann, Zygmunt L. Szpak, Wojciech Chojnacki +1
Evolutionary algorithms have recently been used to create a wide range of artistic work. In this paper, we propose a new approach for the composition of new images from existing on…
Geometry of Moduli Stacks of -stable vector bundles over an algebraic curve
O. Mata-Gutiérrez, Frank Neumann
We study the geometry of the moduli stack of vector bundles of fixed rank and degree over an algebraic curve by introducing a filtration made of open substacks build from -…
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…
Predicting the Energy Output of Wind Farms Based on Weather Data: Important Variables and their Correlation
Katya Vladislavleva, Tobias Friedrich, Frank Neumann +1
Wind energy plays an increasing role in the supply of energy world-wide. The energy output of a wind farm is highly dependent on the weather condition present at the wind farm. If…
Archive-based Single-Objective Evolutionary Algorithms for Submodular Optimization
Frank Neumann, Günter Rudolph
Constrained submodular optimization problems play a key role in the area of combinatorial optimization as they capture many NP-hard optimization problems. So far, Pareto optimizati…
Evolving Diverse Sets of Tours for the Travelling Salesperson Problem
Anh Viet Do, Jakob Bossek, Aneta Neumann +1
Evolving diverse sets of high quality solutions has gained increasing interest in the evolutionary computation literature in recent years. With this paper, we contribute to this ar…
Gabriel-Zisman Cohomology and spectral sequences
Imma Gálvez-Carrillo, Frank Neumann, Andrew Tonks
Extending constructions by Gabriel and Zisman, we develop a functorial framework for the cohomology and homology of simplicial sets with very general coefficient systems given by f…
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…
Theoretical Study of Optimizing Rugged Landscapes with the cGA
Tobias Friedrich, Timo Kötzing, Frank Neumann +1
Estimation of distribution algorithms (EDAs) provide a distribution - based approach for optimization which adapts its probability distribution during the run of the algorithm. We…
Single- and Multi-Objective Evolutionary Algorithms for the Knapsack Problem with Dynamically Changing Constraints
Vahid Roostapour, Aneta Neumann, Frank Neumann
Evolutionary algorithms are bio-inspired algorithms that can easily adapt to changing environments. Recent results in the area of runtime analysis have pointed out that algorithms…
On the number of rational points of classifying stacks for Chevalley group schemes
Scott Balchin, Frank Neumann
We compute the number of rational points of classifying stacks of Chevalley group schemes using the Lefschetz-Grothendieck trace formula of Behrend for -adic cohomology of al…
Enhanced Genetic Programming Models with Multiple Equations for Accurate Semi-Autogenous Grinding Mill Throughput Prediction
Zahra Ghasemi, Mehdi Nesht, Chris Aldrich +4
Semi-autogenous grinding (SAG) mills play a pivotal role in the grinding circuit of mineral processing plants. Accurate prediction of SAG mill throughput as a crucial performance m…
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…
Evolving Hard Maximum Cut Instances for Quantum Approximate Optimization Algorithms
Shuaiqun Pan, Yash J. Patel, Aneta Neumann +3
Variational quantum algorithms, such as the Recursive Quantum Approximate Optimization Algorithm (RQAOA), have become increasingly popular, offering promising avenues for employing…
A General Method to Find Highly Coordinating Communities in Social Media through Inferred Interaction Links
Derek Weber, Frank Neumann
Political misinformation, astroturfing and organised trolling are online malicious behaviours with significant real-world effects. Many previous approaches examining these phenomen…
Computational Complexity Analysis of Simple Genetic Programming On Two Problems Modeling Isolated Program Semantics
Greg Durrett, Frank Neumann, Una-May O'Reilly
Analyzing the computational complexity of evolutionary algorithms for binary search spaces has significantly increased their theoretical understanding. With this paper, we start th…
A Hybrid Intelligent Framework for Maximising SAG Mill Throughput: An Integration of Expert Knowledge, Machine Learning and Evolutionary Algorithms for Parameter Optimisation
Zahra Ghasemi, Mehdi Neshat, Chris Aldrich +4
In mineral processing plants, grinding is a crucial step, accounting for approximately 50 percent of the total mineral processing costs. Semi-autogenous grinding mills are extensiv…
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…
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…
Cohomology of the Moduli Stacks of Real Vector Bundles on Type I Real Algebraic Curves
Luca Dal Molin, Frank Neumann
We study the moduli stacks of real vector bundles of fixed rank and degree on a type I real algebraic curve and determine its mod cohomology algebra in terms of characteristic…
Advanced Ore Mine Optimisation under Uncertainty Using Evolution
William Reid, Aneta Neumann, Simon Ratcliffe +1
In this paper, we investigate the impact of uncertainty in advanced ore mine optimisation. We consider Maptek's software system Evolution which optimizes extraction sequences based…
Who's in the Gang? Revealing Coordinating Communities in Social Media
Derek Weber, Frank Neumann
Political astroturfing and organised trolling are online malicious behaviours with significant real-world effects. Common approaches examining these phenomena focus on broad campai…
Co-Evolutionary Diversity Optimisation for the Traveling Thief Problem
Adel Nikfarjam, Aneta Neumann, Jakob Bossek +1
Recently different evolutionary computation approaches have been developed that generate sets of high quality diverse solutions for a given optimisation problem. Many studies have…
A Fast and Effective Local Search Algorithm for Optimizing the Placement of Wind Turbines
Markus Wagner, Jareth Day, Frank Neumann
The placement of wind turbines on a given area of land such that the wind farm produces a maximum amount of energy is a challenging optimization problem. In this article, we tackle…
Runtime Analysis of RLS and (1+1) EA for the Dynamic Weighted Vertex Cover Problem
Mojgan Pourhassan, Vahid Roostapour, Frank Neumann
In this paper, we perform theoretical analyses on the behaviour of an evolutionary algorithm and a randomised search algorithm for the dynamic vertex cover problem based on its dua…
A Novel Feature-Based Approach to Characterize Algorithm Performance for the Traveling Salesman Problem
Olaf Mersmann, Bernd Bischl, Heike Trautmann +2
Meta-heuristics are frequently used to tackle NP-hard combinatorial optimization problems. With this paper we contribute to the understanding of the success of 2-opt based local se…
Time Complexity Analysis of Evolutionary Algorithms for 2-Hop (1,2)-Minimum Spanning Tree Problem
Feng Shi, Frank Neumann, Jianxin Wang
The Minimum Spanning Tree problem (abbr. MSTP) is a well-known combinatorial optimization problem that has been extensively studied by the researchers in the field of evolutionary…
Scalable Edge Blocking Algorithms for Defending Active Directory Style Attack Graphs
Mingyu Guo, Max Ward, Aneta Neumann +2
Active Directory (AD) is the default security management system for Windows domain networks. An AD environment naturally describes an attack graph where nodes represent computers/a…
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…
A Fully Polynomial Time Approximation Scheme for Packing While Traveling
Frank Neumann, Sergey Polyakovskiy, Martin Skutella +2
Understanding the interactions between different combinatorial optimisation problems in real-world applications is a challenging task. Recently, the traveling thief problem (TTP),…