Surveys, Benchmarking, Tutorials in Computational Intelligence
1. B.Y.Qu, Y.S.Zhu, Y.C.Jiao, M.Y.Wu, P.N.Suganthan, J.J.Liang, A Survey on Multi-objective Evolutionary Algorithms for the Solution of the Environmental/Economic Dispatch Problems, Swarm and Evolutionary Computation, Feb 2018. https://doi.org/10.1016/j.swevo.2017.06.002
2. L. Zhang, P. N. Suganthan, Benchmarking Ensemble Classifiers with Novel Co-Trained Kernel Ridge Regression and Random Vector Functional Link Ensembles, IEEE Computational Intelligence Magazine, Nov 2017.
3. A. Rajasekhar, N. Lynn, S. Das, and P. N. Suganthan, "Computing with the Collective Intelligence of Honey Bees A Survey," Swarm and Evolutionary Computation, pp. 25-48, Feb 2017. (supplementary file available here) https://doi.org/10.1016/j.swevo.2016.06.001
4. Adam P. Piotrowski, Review of Differential Evolution population size, Swarm and Evolutionary Computation, pp. 1-24, Feb 2017.
5. Emrah Hancer, Dervis Karaboga, A comprehensive survey of traditional, merge-split and evolutionary approaches proposed for determination of cluster number, pp. 49-67, Swarm and Evolutionary Computation, Feb 2017.
6. Akhilesh Gotmare, Sankha Subhra Bhattacharjee, Rohan Patidar, Nithin V. George, Swarm and evolutionary computing algorithms for system identification and filter design: A comprehensive review, pp. 68-84, Swarm and Evolutionary Computation, Feb 2017.
7. Ruchika Malhotra, Megha Khanna, Rajeev R. Raje, On the application of search-based techniques for software engineering predictive modeling: A systematic review and future directions, pp. 85-109, Swarm and Evolutionary Computation, Feb 2017.
8. Michalis Mavrovouniotis, Changhe Li, Shengxiang Yang, A survey of swarm intelligence for dynamic optimization: Algorithms and applications, Swarm and Evolutionary Computation, pp. 1-17, April 2017.
9. L. Zhang, P. N. Suganthan, "A Survey of Randomized Algorithms for Training Neural Networks," Information Sciences, DoI: 10.1016/j.ins.2016.01.039, October, 2016. Also the editorial of this special issue on "Randomized Algorithms for Training Neural Networks" in Information Sciences. at https://www.researchgate.net/publication/303392461_Editorial_Randomized_Algorithms_for_Training_Neural_Networks by Dr Dianhui Wang.
10. S. Das, S. S. Mullick, P. N. Suganthan, "Recent Advances in Differential Evolution - An Updated Survey," Swarm and Evolutionary Computation, pp. 1-30, April 2016. https://doi.org/10.1016/j.swevo.2016.01.004
11. Y. Ren, L. Zhang, and P. N. Suganthan, "Ensemble Classification and Regression Recent Developments, Applications and Future Directions," IEEE Computational Intelligence Magazine, DOI: 10.1109/MCI.2015.2471235 , Feb 2016.
12. I Fister, K Ljubič, PN Suganthan, M Perc, "Computational intelligence in sports: Challenges and opportunities within a new research domain," Applied Mathematics and Computation 262, 178-186. 2015.
13. P. N. Suganthan, "Numerical Optimization by Nature Inspired Algorithms", keynote lecture at ICHSA 2015, Korea University, Seoul, 19th 21st Aug. 2015.
14. Rammohan Mallipeddi, P. N. Suganthan, "Unit commitment a survey and comparison of conventional and nature inspired algorithms", Int. J. Bio-Inspired Computation, Vol. 6, No. 2, 2014