136 citations · 418 across the 27 of their papers we have counts for
6 papers · 1 filter
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…