All Outputs (23)

Classification of osteoarthritic and healthy cartilage using deep learning with Raman spectra (2024)
Journal Article

Raman spectroscopy is a rapid method for analysing the molecular composition of biological material. However, noise contamination in the spectral data necessitates careful pre-processing prior to analysis. Here we propose an end-to-end Convolutional... Read More about Classification of osteoarthritic and healthy cartilage using deep learning with Raman spectra.

Multi-objective topology optimisation for acoustic porous materials using gradient-based, gradient-free, and hybrid strategies (2023)
Journal Article

When designing passive sound-attenuation structures, one of the challenging problems that arise is optimally distributing acoustic porous materials within a design region so as to maximise sound absorption while minimising material usage. To identify... Read More about Multi-objective topology optimisation for acoustic porous materials using gradient-based, gradient-free, and hybrid strategies.

Comparison of heuristics and metaheuristics for topology optimisation in acoustic porous materials (2021)
Journal Article

When designing sound packages, often fully filling the available space with acoustic materials is not the most absorbing solution. Better solutions can be obtained by creating cavities of air pockets, but determining the most optimal shape and topolo... Read More about Comparison of heuristics and metaheuristics for topology optimisation in acoustic porous materials.

Acoustic topology optimisation using CMA-ES (2020)
Conference Proceeding

Structural topology optimisation techniques are increasingly being applied to acoustic materials. Most acoustic topology optimisation applications use the solid-isotropic-material-with-penalization (SIMP) approach [1]–[4] which is a derivative-based... Read More about Acoustic topology optimisation using CMA-ES.

A hybrid combinatorial approach to a two-stage stochastic portfolio optimization model with uncertain asset prices (2019)
Journal Article

© 2019, Springer-Verlag GmbH Germany, part of Springer Nature. Portfolio optimization is one of the most important problems in the finance field. The traditional Markowitz mean-variance model is often unrealistic since it relies on the perfect market... Read More about A hybrid combinatorial approach to a two-stage stochastic portfolio optimization model with uncertain asset prices.

Metaheuristic optimisation of sound absorption performance of multilayered porous materials (2019)
Conference Proceeding

The optimization of multilayered-sound-packaging is a challenging task which involves searching the best/op-timal settings for a number of acoustic parameters. The search space size becomes too large to handle by brute force, as the number of those p... Read More about Metaheuristic optimisation of sound absorption performance of multilayered porous materials.

Pattern-Based Approach to the Workflow Satisfiability Problem with User-Independent Constraints (2019)
Journal Article

The fixed parameter tractable (FPT) approach is a powerful tool in tackling computationally hard problems. In this paper, we link FPT results to classic artificial intelligence (AI) techniques to show how they complement each other. Specifically, we... Read More about Pattern-Based Approach to the Workflow Satisfiability Problem with User-Independent Constraints.

A Hybrid Evolutionary Strategy to Optimise Early-Stage Cancer Screening (2019)
Conference Proceeding

Current methods to identify cutoff values for tumour-associated molecules (antigens) discrimination are based on statistics and brute force. These methods applied to cancer screening problems are very inefficient, especially with large data sets with... Read More about A Hybrid Evolutionary Strategy to Optimise Early-Stage Cancer Screening.

Learning the Quality of Dispatch Heuristics Generated by Automated Programming (2018)
Book Chapter

One of the challenges within the area of optimisation, and AI in general, is to be able to support the automated creation of the heuristics that are often needed within effective algorithms. Such an example of automated programming may be performed b... Read More about Learning the Quality of Dispatch Heuristics Generated by Automated Programming.

Algorithm Configuration: Learning Policies for the Quick Termination of Poor Performers (2018)
Conference Proceeding

© 2019, Springer Nature Switzerland AG. One way to speed up the algorithm configuration task is to use short runs instead of long runs as much as possible, but without discarding the configurations that eventually do well on the long runs. We conside... Read More about Algorithm Configuration: Learning Policies for the Quick Termination of Poor Performers.

Exploring the landscape of the space of heuristics for local search in SAT (2017)
Presentation / Conference Contribution
Burnett, A. W., & Parkes, A. J. (2017). Exploring the landscape of the space of heuristics for local search in SAT.

Local search is a powerful technique on many combinatorial optimisation problems. However, the effectiveness of local search methods will often depend strongly on the details of the heuristics used within them. There are many potential heuristics, an... Read More about Exploring the landscape of the space of heuristics for local search in SAT.

International Portfolio Optimisation with Integrated Currency Overlay Costs and Constraints (2017)
Journal Article
Chatsanga, N., & Parkes, A. J. (2017). International Portfolio Optimisation with Integrated Currency Overlay Costs and Constraints. Expert Systems with Applications, 83, 333-349. https://doi.org/10.1016/j.eswa.2017.04.009

International financial portfolios can be exposed to substantial risk from variations of the exchange rates between the countries in which they hold investments. Nonetheless, foreign exchange can both generate extra return as well as loss to a portfo... Read More about International Portfolio Optimisation with Integrated Currency Overlay Costs and Constraints.

Fairness in examination timetabling: student preferences and extended formulations (2017)
Journal Article
Muklason, A., Parkes, A. J., Özcan, E., McCollum, B., & McMullan, P. (2017). Fairness in examination timetabling: student preferences and extended formulations. Applied Soft Computing, 55, 302-318. https://doi.org/10.1016/j.asoc.2017.01.026

Variations of the examination timetabling problem have been investigated by the research community for more than two decades. The common characteristic between all problems is the fact that the definitions and data sets used all originate from actual... Read More about Fairness in examination timetabling: student preferences and extended formulations.

Markov Chain methods for the Bipartite Boolean Quadratic Programming Problem (2017)
Journal Article
Karapetyan, D., Punnen, A., & Parkes, A. J. (2017). Markov Chain methods for the Bipartite Boolean Quadratic Programming Problem. European Journal of Operational Research, 260(2), 494-506. https://doi.org/10.1016/j.ejor.2017.01.001

We study the Bipartite Boolean Quadratic Programming Problem (BBQP) which is an extension of the well known Boolean Quadratic Programming Problem (BQP). Applications of the BBQP include mining discrete patterns from binary data, approximating matrice... Read More about Markov Chain methods for the Bipartite Boolean Quadratic Programming Problem.

Systematic search for local-search SAT heuristics (2016)
Presentation / Conference Contribution
Burnett, A. W., & Parkes, A. J. (2016). Systematic search for local-search SAT heuristics.

Heuristics for local-search are a commonly used method of improving the performance of algorithms that solve hard computational problems. Generally these are written by human experts, however a long-standing research goal has been to automate the con... Read More about Systematic search for local-search SAT heuristics.

Combining Monte-Carlo and hyper-heuristic methods for the multi-mode resource-constrained multi-project scheduling problem (2016)
Journal Article
Asta, S., Karapetyan, D., Kheiri, A., Özcan, E., & Parkes, A. J. (2016). Combining Monte-Carlo and hyper-heuristic methods for the multi-mode resource-constrained multi-project scheduling problem. Information Sciences, 373, 476-498. https://doi.org/10.1016/j.ins.2016.09.010

Multi-mode resource and precedence-constrained project scheduling is a well-known challenging real-world optimisation problem. An important variant of the problem requires scheduling of activities for multiple projects considering availability of loc... Read More about Combining Monte-Carlo and hyper-heuristic methods for the multi-mode resource-constrained multi-project scheduling problem.

CHAMP: Creating Heuristics via Many Parameters for online bin packing (2016)
Journal Article
Asta, S., Özcan, E., & Parkes, A. J. (2016). CHAMP: Creating Heuristics via Many Parameters for online bin packing. Expert Systems with Applications, 63, 208-221. https://doi.org/10.1016/j.eswa.2016.07.005

The online bin packing problem is a well-known bin packing variant which requires immediate decisions to be made for the placement of a lengthy sequence of arriving items of various sizes one at a time into fixed capacity bins without any overflow. T... Read More about CHAMP: Creating Heuristics via Many Parameters for online bin packing.

Lessons from building an automated pre-departure sequencer for airports (2015)
Journal Article
Atkin, J. A. D., Karapetyan, D., Parkes, A. J., & Castro-Gutierrez, J. (2015). Lessons from building an automated pre-departure sequencer for airports. Annals of Operations Research, 252(2), 435-453. https://doi.org/10.1007/s10479-015-1960-z

© 2015, Springer Science+Business Media New York. Commercial airports are under increasing pressure to comply with the Eurocontrol collaborative decision making (CDM) initiative, to ensure that information is passed between stakeholders, integrate au... Read More about Lessons from building an automated pre-departure sequencer for airports.

A software interface for supporting the application of data science to optimisation (2015)
Journal Article
Parkes, A. J., Özcan, E., & Karapetyan, D. (2015). A software interface for supporting the application of data science to optimisation. Lecture Notes in Artificial Intelligence, 8994, 306-311. https://doi.org/10.1007/978-3-319-19084-6_31

Many real world problems can be solved effectively by metaheuristics in combination with neighbourhood search. However, implementing neighbourhood search for a particular problem domain can be time consuming and so it is important to get the most val... Read More about A software interface for supporting the application of data science to optimisation.

Heuristic generation via parameter tuning for online bin packing (2015)
Presentation / Conference Contribution
Yarimcam, A., Asta, S., Ozcan, E., & Parkes, A. J. (2015). Heuristic generation via parameter tuning for online bin packing. In 2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS) (102-108). https://doi.org/10.1109/EALS.2014.7009510

© 2014 IEEE. Online bin packing requires immediate decisions to be made for placing an incoming item one at a time into bins of fixed capacity without causing any overflow. The goal is to maximise the average bin fullness after placement of a long st... Read More about Heuristic generation via parameter tuning for online bin packing.