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 ...