23 citations · 89 across the 19 of their papers we have counts for
19 papers
Analysis of modular CMA-ES on strict box-constrained problems in the SBOX-COST benchmarking suite
Diederick Vermetten, Manuel López-Ibáñez, Olaf Mersmann +2
Box-constraints limit the domain of decision variables and are common in real-world optimization problems, for example, due to physical, natural or spatial limitations. Consequentl…
Applying Ising Machines to Multi-objective QUBOs
Mayowa Ayodele, Richard Allmendinger, Manuel López-Ibáñez +2
Multi-objective optimisation problems involve finding solutions with varying trade-offs between multiple and often conflicting objectives. Ising machines are physical devices that…
A Study of Scalarisation Techniques for Multi-Objective QUBO Solving
Mayowa Ayodele, Richard Allmendinger, Manuel López-Ibáñez +1
In recent years, there has been significant research interest in solving Quadratic Unconstrained Binary Optimisation (QUBO) problems. Physics-inspired optimisation algorithms have…
Comparing the Utility and Disclosure Risk of Synthetic Data with Samples of Microdata
Claire Little, Mark Elliot, Richard Allmendinger
Most statistical agencies release randomly selected samples of Census microdata, usually with sample fractions under 10% and with other forms of statistical disclosure control (SDC…
Combining Topic Modeling with Grounded Theory: Case Studies of Project Collaboration
Eyyub Can Odacioglu, Lihong Zhang, Richard Allmendinger
This paper proposes an Artificial Intelligence (AI) Grounded Theory for management studies. We argue that this novel and rigorous approach that embeds topic modelling will lead to…
Cooperative Multi-Agent Search on Endogenously-Changing Fitness Landscapes
Chin Woei Lim, Richard Allmendinger, Joshua Knowles +2
We use a multi-agent system to model how agents (representing firms) may collaborate and adapt in a business 'landscape' where some, more influential, firms are given the power to…
Efficient Approximation of Expected Hypervolume Improvement using Gauss-Hermite Quadrature
Alma Rahat, Tinkle Chugh, Jonathan Fieldsend +2
Many methods for performing multi-objective optimisation of computationally expensive problems have been proposed recently. Typically, a probabilistic surrogate for each objective…
Detection of Hidden Objectives and Interactive Objective Reduction
Seyed Mahdi Shavarani, Manuel López-Ibáñez, Richard Allmendinger
In multi-objective optimization problems, there might exist hidden objectives that are important to the decision-maker but are not being optimized. On the other hand, there might a…
Multi-objective QUBO Solver: Bi-objective Quadratic Assignment
Mayowa Ayodele, Richard Allmendinger, Manuel López-Ibáñez +1
Quantum and quantum-inspired optimisation algorithms are designed to solve problems represented in binary, quadratic and unconstrained form. Combinatorial optimisation problems are…
A Data-Driven Framework for Identifying Investment Opportunities in Private Equity
Samantha Petersone, Alwin Tan, Richard Allmendinger +2
The core activity of a Private Equity (PE) firm is to invest into companies in order to provide the investors with profit, usually within 4-7 years. To invest into a company or not…
Are Evolutionary Algorithms Safe Optimizers?
Youngmin Kim, Richard Allmendinger, Manuel López-Ibáñez
We consider a type of constrained optimization problem, where the violation of a constraint leads to an irrevocable loss, such as breakage of a valuable experimental resource/platf…
SonOpt: Sonifying Bi-objective Population-Based Optimization Algorithms
Tasos Asonitis, Richard Allmendinger, Matt Benatan +1
We propose SonOpt, the first (open source) data sonification application for monitoring the progress of bi-objective population-based optimization algorithms during search, to faci…
Generative Adversarial Networks for Synthetic Data Generation: A Comparative Study
Claire Little, Mark Elliot, Richard Allmendinger +1
Generative Adversarial Networks (GANs) are gaining increasing attention as a means for synthesising data. So far much of this work has been applied to use cases outside of the data…
Towards a fairer reimbursement system for burn patients using cost-sensitive classification
Chimdimma Noelyn Onah, Richard Allmendinger, Julia Handl +1
The adoption of the Prospective Payment System (PPS) in the UK National Health Service (NHS) has led to the creation of patient groups called Health Resource Groups (HRG). HRGs aim…
What if we Increase the Number of Objectives? Theoretical and Empirical Implications for Many-objective Optimization
Richard Allmendinger, Andrzej Jaszkiewicz, Arnaud Liefooghe +1
The difficulty of solving a multi-objective optimization problem is impacted by the number of objectives to be optimized. The presence of many objectives typically introduces a num…
Managing Manufacturing and Delivery of Personalised Medicine: Current and Future Models
Andreea Avramescu, Richard Allmendinger, Manuel López-Ibáñez
With almost 50% of annual commercial drug approvals being Personalised Medicine (PM) and its huge potential to improve quality of life, this emerging medical sector has received in…
Heterogeneous Objectives: State-of-the-Art and Future Research
Richard Allmendinger, Joshua Knowles
Multiobjective optimization problems with heterogeneous objectives are defined as those that possess significantly different types of objective function components (not just incomm…
HAWKS: Evolving Challenging Benchmark Sets for Cluster Analysis
Cameron Shand, Richard Allmendinger, Julia Handl +2
Comprehensive benchmarking of clustering algorithms is rendered difficult by two key factors: (i)~the elusiveness of a unique mathematical definition of this unsupervised learning…
Safe Learning and Optimization Techniques: Towards a Survey of the State of the Art
Youngmin Kim, Richard Allmendinger, Manuel López-Ibáñez
Safe learning and optimization deals with learning and optimization problems that avoid, as much as possible, the evaluation of non-safe input points, which are solutions, policies…