Skip to main content

Research Repository

Advanced Search

All Outputs (8)

A Feature-Free Approach to Automated Algorithm Selection (2023)
Conference Proceeding
Alissa, M., Sim, K., & Hart, E. (2023). A Feature-Free Approach to Automated Algorithm Selection. In GECCO '23 Companion: Proceedings of the Companion Conference on Genetic and Evolutionary Computation (9-10). https://doi.org/10.1145/3583133.3595832

This article summarises recent work in the domain of feature-free algorithm selection that was published in the Journal of Heuristics in January 2023, with the title 'Automated Algorithm Selection: from Feature-Based to Feature-Free Approaches'. Spec... Read More about A Feature-Free Approach to Automated Algorithm Selection.

Automated Algorithm Selection: from Feature-Based to Feature-Free Approaches (2023)
Journal Article
Alissa, M., Sim, K., & Hart, E. (2023). Automated Algorithm Selection: from Feature-Based to Feature-Free Approaches. Journal of Heuristics, 29(1), 1-38. https://doi.org/10.1007/s10732-022-09505-4

We propose a novel technique for algorithm-selection, applicable to optimisation domains in which there is implicit sequential information encapsulated in the data, e.g., in online bin-packing. Specifically we train two types of recurrent neural netw... Read More about Automated Algorithm Selection: from Feature-Based to Feature-Free Approaches.

From algorithm selection to generation using deep learning (2022)
Thesis
Alissa, M. From algorithm selection to generation using deep learning. (Thesis). Edinburgh Napier University. Retrieved from http://researchrepository.napier.ac.uk/Output/2952201

Algorithm selection and generation techniques are two methods that can be used to exploit the performance complementarity of different algorithms when applied to large diverse sets of combinatorial problem instances. As there is no single algorithm t... Read More about From algorithm selection to generation using deep learning.

Parkinson’s disease diagnosis using convolutional neural networks and figure-copying tasks (2021)
Journal Article
Alissa, M., Lones, M. A., Cosgrove, J., Alty, J. E., Jamieson, S., Smith, S. L., & Vallejo, M. (2022). Parkinson’s disease diagnosis using convolutional neural networks and figure-copying tasks. Neural Computing and Applications, 34, 1433-1453. https://doi.org/10.1007/s00521-021-06469-7

Parkinson’s disease (PD) is a progressive neurodegenerative disorder that causes abnormal movements and an array of other symptoms. An accurate PD diagnosis can be a challenging task as the signs and symptoms, particularly at an early stage, can be s... Read More about Parkinson’s disease diagnosis using convolutional neural networks and figure-copying tasks.

A Neural Approach to Generation of Constructive Heuristics (2021)
Conference Proceeding
Alissa, M., Sim, K., & Hart, E. (2021). A Neural Approach to Generation of Constructive Heuristics. In 2021 IEEE Congress on Evolutionary Computation (CEC) (1147-1154). https://doi.org/10.1109/CEC45853.2021.9504989

Both algorithm-selection methods and hyper-heuristic methods rely on a pool of complementary heuristics. Improving the pool with new heuristics can improve performance, however, designing new heuristics can be challenging. Methods such as genetic pro... Read More about A Neural Approach to Generation of Constructive Heuristics.

TRUSTD: Combat Fake Content using Blockchain and Collective Signature Technologies (2020)
Conference Proceeding
Jaroucheh, Z., Alissa, M., Buchanan, W. J., & Liu, X. (2020). TRUSTD: Combat Fake Content using Blockchain and Collective Signature Technologies. In 2020 IEEE 44th Annual Computers, Software, and Applications Conference (COMPSAC 2020) (1215-1220)

The growing trend of sharing news/contents, through social media platforms and the World Wide Web has been seen to impact our perception of the truth, altering our views about politics, economics, relationships, needs and wants. This is because of th... Read More about TRUSTD: Combat Fake Content using Blockchain and Collective Signature Technologies.

A Deep Learning Approach to Predicting Solutions in Streaming Optimisation Domains (2020)
Conference Proceeding
Alissa, M., Sim, K., & Hart, E. (2020). A Deep Learning Approach to Predicting Solutions in Streaming Optimisation Domains. . https://doi.org/10.1145/3377930.3390224

In the field of combinatorial optimisation, per-instance algorithm selection still remains a challenging problem, particularly with respect to streaming problems such as packing or scheduling. Typical approaches involve training a model to predict th... Read More about A Deep Learning Approach to Predicting Solutions in Streaming Optimisation Domains.

Algorithm selection using deep learning without feature extraction (2019)
Conference Proceeding
Alissa, M., Sim, K., & Hart, E. (2019). Algorithm selection using deep learning without feature extraction. In GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference Companion (198-206). https://doi.org/10.1145/3321707.3321845

We propose a novel technique for algorithm-selection which adopts a deep-learning approach, specifically a Recurrent-Neural Network with Long-Short-Term-Memory (RNN-LSTM). In contrast to the majority of work in algorithm-selection, the approach does... Read More about Algorithm selection using deep learning without feature extraction.