136 citations · 418 across the 27 of their papers we have counts for
27 papers
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…
On Single-Objective Sub-Graph-Based Mutation for Solving the Bi-Objective Minimum Spanning Tree Problem
Jakob Bossek, Christian Grimme
We contribute to the efficient approximation of the Pareto-set for the classical -hard multi-objective minimum spanning tree problem (moMST) adopting evolutionary com…
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…
Runtime Analysis of Quality Diversity Algorithms
Jakob Bossek, Dirk Sudholt
Quality diversity~(QD) is a branch of evolutionary computation that gained increasing interest in recent years. The Map-Elites QD approach defines a feature space, i.e., a partitio…
Exploring the Feature Space of TSP Instances Using Quality Diversity
Jakob Bossek, Frank Neumann
Generating instances of different properties is key to algorithm selection methods that differentiate between the performance of different solvers for a given combinatorial optimiz…
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…
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…
Time Complexity Analysis of Randomized Search Heuristics for the Dynamic Graph Coloring Problem
Jakob Bossek, Frank Neumann, Pan Peng +1
We contribute to the theoretical understanding of randomized search heuristics for dynamic problems. We consider the classical vertex coloring problem on graphs and investigate the…
Generating Instances with Performance Differences for More Than Just Two Algorithms
Jakob Bossek, Markus Wagner
In recent years, Evolutionary Algorithms (EAs) have frequently been adopted to evolve instances for optimization problems that pose difficulties for one algorithm while being rathe…
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…
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 Diversity Optimization and the Minimum Spanning Tree Problem
Jakob Bossek, Frank Neumann
In the area of evolutionary computation the calculation of diverse sets of high-quality solutions to a given optimization problem has gained momentum in recent years under the term…
Benchmarking in Optimization: Best Practice and Open Issues
Thomas Bartz-Beielstein, Carola Doerr, Daan van den Berg +14
This survey compiles ideas and recommendations from more than a dozen researchers with different backgrounds and from different institutes around the world. Promoting best practice…
Deep Learning as a Competitive Feature-Free Approach for Automated Algorithm Selection on the Traveling Salesperson Problem
Moritz Seiler, Janina Pohl, Jakob Bossek +2
In this work we focus on the well-known Euclidean Traveling Salesperson Problem (TSP) and two highly competitive inexact heuristic TSP solvers, EAX and LKH, in the context of per-i…
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…
Dynamic Bi-Objective Routing of Multiple Vehicles
Jakob Bossek, Christian Grimme, Heike Trautmann
In practice, e.g. in delivery and service scenarios, Vehicle-Routing-Problems (VRPs) often imply repeated decision making on dynamic customer requests. As in classical VRPs, tours…
Towards Decision Support in Dynamic Bi-Objective Vehicle Routing
Jakob Bossek, Christian Grimme, Günter Rudolph +1
We consider a dynamic bi-objective vehicle routing problem, where a subset of customers ask for service over time. Therein, the distance traveled by a single vehicle and the number…
More Effective Randomized Search Heuristics for Graph Coloring Through Dynamic Optimization
Jakob Bossek, Frank Neumann, Pan Peng +1
Dynamic optimization problems have gained significant attention in evolutionary computation as evolutionary algorithms (EAs) can easily adapt to changing environments. We show that…
Anytime Behavior of Inexact TSP Solvers and Perspectives for Automated Algorithm Selection
Jakob Bossek, Pascal Kerschke, Heike Trautmann
The Traveling-Salesperson-Problem (TSP) is arguably one of the best-known NP-hard combinatorial optimization problems. The two sophisticated heuristic solvers LKH and EAX and respe…
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…
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…
Initial Design Strategies and their Effects on Sequential Model-Based Optimization
Jakob Bossek, Carola Doerr, Pascal Kerschke
Sequential model-based optimization (SMBO) approaches are algorithms for solving problems that require computationally or otherwise expensive function evaluations. The key design p…
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…