4 citations · 7 across the 19 of their papers we have counts for
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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…
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…
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…