All Outputs (7)

Acoustic topology optimisation using CMA-ES (2020)
Conference Proceeding
Ramamoorthy, V. T., Ozcan, E., Parkes, A., Sreekumar, A., Jaouen, L., & Becot, F. (2020). Acoustic topology optimisation using CMA-ES. In Proceedings of ISMA2020 International Conference on Noise and Vibration Engineering: USD2020 International Conference on Uncertainty in Structural Dynamics

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.

Metaheuristic optimisation of sound absorption performance of multilayered porous materials (2019)
Conference Proceeding
Ramamoorthy, V. T., Özcan, E., Parkes, A. J., Luc, J., & Bécot, F. (2019). Metaheuristic optimisation of sound absorption performance of multilayered porous materials. In Proceedings of the ICA 2019 and EAA Euroregio 23rd International Congress on Acoustics, integrating 4th EAA Euroregio 2019 9 - 13 September 2019, Aachen, Germany (3213-3220)

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.

A Hybrid Evolutionary Strategy to Optimise Early-Stage Cancer Screening (2019)
Conference Proceeding
Figueredo, G. P., Shi, P., Parkes, A. J., Evans, K., Garibaldi, J. M., Negm, O., …Robertson, J. (2019). A Hybrid Evolutionary Strategy to Optimise Early-Stage Cancer Screening. In 2019 IEEE Congress on Evolutionary Computation (CEC) (95-102). https://doi.org/10.1109/CEC.2019.8790316

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.

Algorithm Configuration: Learning Policies for the Quick Termination of Poor Performers (2018)
Conference Proceeding
Karapetyan, D., Parkes, A. J., & Stützle, T. (2019). Algorithm Configuration: Learning Policies for the Quick Termination of Poor Performers. In Learning and Intelligent Optimization 12th International Conference, LION 12, Kalamata, Greece, June 10–15, 2018, Revised Selected Papers (220-224). https://doi.org/10.1007/978-3-030-05348-2_20

© 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)
Conference Proceeding
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.

Systematic search for local-search SAT heuristics (2016)
Conference Proceeding
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.

Heuristic generation via parameter tuning for online bin packing (2015)
Conference Proceeding
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.