Automated Algorithm Selection: from Feature-Based to Feature-Free Approaches
Journal Article
Alissa, M., Sim, K., & Hart, E. (in press)
Automated Algorithm Selection: from Feature-Based to Feature-Free Approaches. Journal of Heuristics, 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 o...
From algorithm selection to generation using deep learning
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...
Parkinson’s disease diagnosis using convolutional neural networks and figure-copying tasks
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 challengi...
A Neural Approach to Generation of Constructive Heuristics
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, howeve...
TRUSTD: Combat Fake Content using Blockchain and Collective Signature Technologies
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...
A Deep Learning Approach to Predicting Solutions in Streaming Optimisation Domains
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 packi...
Algorithm selection using deep learning without feature extraction
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 ...