5 citations · 5 across the 6 of their papers we have counts for
8 papers · 1 filter
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…
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…
Evolutionary Algorithms for Generating Graphs Matching Desired Laplacian Spectra
Hendrik Richter, Frank Neumann
Graphs with diverse structural characteristics play a central role in modelling and optimization tasks. The ability to generate different types of graphs that exhibit shared proper…
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…