Almeida, A., De Villiers, J.P., De Freitas, A. & Velayudan, M. 2022. “The complementarity of a diverse range of deep learning features extracted from video content for video recommendation”. Expert Systems with Applications, Vol 192.
Beckedahl, D. & and Pillay, N. 2020. A study of bi-space search for solving the one-dimensional bin packing problem. 19th International Conference on Artificial Intelligence and Soft Computing, Lecture Notes in Artificial Intelligence, Vol. 12416, 277–289.
Beckedahl, D. & Pillay, N. 2022. Bi-space search: Optimising the hybridisation of search spaces in solving the one-dimensional bin packing problem, International Conference on Artificial Intelligence and Soft Computing, Lecture Notes in Artificial Intelligence, accepted.
Hassan, A. & Pillay, N. 2021. Dynamic heuristic set selection for cross-domain selection hyper-heuristics. Lecture Notes in Computer Science: Theory and Practice of Natural Computing, Tenth International Conference, December 2021, pp. 33–46.
Hassan, A. & Pillay, N. 2022. Automated design of dynamic heuristic set selection for cross-domain selection hyper-heuristics. International Conference on Artificial Intelligence and Soft Computing, Lecture Notes in Artificial Intelligence, accepted.
Singh, E. & Pillay, N. 2021. Ant-based generation constructive hyper-heuristics for the movie scene scheduling problem. Lecture Notes in Computer Science: Theory and Practice of Natural Computing, Tenth International Conference, December 2021, pp. 109–120.
Singh, E. & Pillay, N. 2021. Ant-based hyper-heuristics for the movie science scheduling problem. 20th International Conference on Artificial Intelligence and Soft Computing, Lecture Notes in Artificial Intelligence, pp. 342–353.
Singh, E. & Pillay, N. 2022. A study of ant-based pheromone spaces for generation constructive hyper-heuristics. Swarm and Evolutionary Computation. Vol 72, July 2022, 101095.
CONFERENCE PROCEEDINGS
Almeida, A., De Villiers, J.P., De Freitas, A. & Velayudan, M. 2020. Visual comparison of statistical feature aggregation methods for video-based similarity applications, 23rd International Conference on Information Fusion, Pretoria, South Africa, 6–9 July 2020, pp. 22–29.
Beckedahl, D., Nel, A. & Pillay, N. 2019. A study of multi-space search optimisation. International Conference on Intelligent Systems Design and Applications: Advances of Intelligent Systems and Computing, Vol. 940, Springer, pp. 1–9.
Gerber, M. & Pillay, N. 2022. Automated design of feature extraction for unsupervised image clustering using grammatical evolution, accepted for publication in the 2022 IEEE Symposium on Computational Intelligence, September.
Hassan, A. & Pillay, N. 2021. An investigation of automated design of VMAF. Proceedings of the 2021 IEEE Symposium Series on Computational Intelligence, pp. 1–8.
Hassan, A. & Pillay, N. 2022. Automated design of hybrid metaheuristics: Fitness landscape analysis. In proceedings of the IEEE World Congress for Computational Intelligence, accepted.
Hassan, A. & Pillay, N. 2022. Hybridising a genetic algorithm with reinforcement learning for automated design of genetic Algorithms. In proceedings of the IEEE World Congress for Computational Intelligence, accepted.
Mervitz, J., De Villiers, J.P., Jacobs, P. and Kloppers, M. 2020. Comparison of early and late fusion techniques for movie trailer genre labelling, 23rd International Conference on Information Fusion, Pretoria, South Africa, 6–9 July 2020, pp. 708–713.
Pretorius, K.W. & Pillay, N. 2020. A comparative study of classifiers for thumbnail selection. Proceedings of the 2020 IEEE International Joint Conference on Neural Networks.
Pretorius, K.W. & Pillay, N. 2021. Population-based reinforcement learning, Proceedings of the 2021 IEEE Symposium Series on Computational Intelligence, pp. 1–8.
Singh, E. & Pillay, N. 2022. A parameter-based analysis of ant-based generation hyper-heuristics, accepted for publication in the 2022 IEEE Symposium on Computational Intelligence, September.
Singh, E. & Pillay, N. 2022. A study of transfer learning in an ant-based generation construction hyper-heuristic. In Proceedings of the IEEE World Congress for Computational Intelligence, accepted.
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