Kyle Harrison

Research Associate

Kyle is a Research Associate with the School of Engineering and Information Technology at the University of New South Wales (UNSW) Canberra, Australia. Previously, he was a Postdoctoral Fellow at the University of Ontario Institute of Technology (Ontario Tech University), Canada. He received his PhD in Computer Science from the University of Pretoria, South Africa, in 2018, and the M.Sc. and B.Sc. degrees in Computer Science from Brock University, Canada, in 2014 and 2012, respectively.

His research interests include computational intelligence, self-adaptive optimization, fitness landscape analysis, operations research, and real-world applications of complex networks. He has co-authored numerous publications in top-tier journals and conferences and serves as a reviewer for various publication venues in the computational intelligence, evolutionary computation, and operations research domains.

Conference Papers
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Harrison KR; Elsayed S; Sarker RA; Garanovich IL; Weir T; Boswell SG, 2021, 'Project portfolio selection with defense capability options', in GECCO 2021 Companion - Proceedings of the 2021 Genetic and Evolutionary Computation Conference Companion, ACM, pp. 1825 - 1826, presented at GECCO '21: Genetic and Evolutionary Computation Conference, http://dx.doi.org/10.1145/3449726.3463126
2021
Harrison KR; Ombuki-Berman BM; Engelbrecht AP, 2021, 'Visualizing and Characterizing the Parameter Configuration Landscape of Particle Swarm Optimization using Physical Landform Classification', in 2021 IEEE Congress on Evolutionary Computation (CEC), IEEE, presented at 2021 IEEE Congress on Evolutionary Computation (CEC), 28 June 2021 - 01 July 2021, http://dx.doi.org/10.1109/cec45853.2021.9504760
2021
Harrison KR; Ombuki-Berman BM; Engelbrecht AP, 2020, 'Visualizing and Characterizing the Parameter Configuration Landscape of Differential Evolution using Physical Landform Classification', in 2020 IEEE Symposium Series on Computational Intelligence, SSCI 2020, IEEE, pp. 2437 - 2444, presented at 2020 IEEE Symposium Series on Computational Intelligence (SSCI), 01 December 2020 - 04 December 2020, http://dx.doi.org/10.1109/SSCI47803.2020.9308536
2020
Harrison KR; Elsayed S; Weir T; Garanovich IL; Galister M; Boswell S; Taylor R; Sarker R, 2020, 'Multi-Period Project Selection and Scheduling for Defence Capability-Based Planning', in IEEE Transactions on Systems, Man, and Cybernetics: Systems, pp. 4044 - 4050, http://dx.doi.org/10.1109/SMC42975.2020.9283334
2020
Harrison KR; Elsayed S; Weir T; Garanovich IL; Taylor R; Sarker R, 2020, 'An Exploration of Meta-Heuristic Approaches for the Project Portfolio Selection and Scheduling Problem in a Defence Context', in 2020 IEEE Symposium Series on Computational Intelligence, SSCI 2020, IEEE, pp. 1395 - 1402, presented at 2020 IEEE Symposium Series on Computational Intelligence (SSCI), 01 December 2020 - 04 December 2020, http://dx.doi.org/10.1109/SSCI47803.2020.9308608
2020
Harrison KR; Ombuki-Berman B; Engelbrecht A, 2019, 'An Analysis of Control Parameter Importance in the Particle Swarm Optimization Algorithm', in Tan Y; Shi Y; Niu B (eds.), Advances in Swarm Intelligence, Springer, Cham, Chiang Mai, Thailand, pp. 93 - 105, presented at The Tenth International Conference on Swarm Intelligence (ICSI’2019), Chiang Mai, Thailand, 26 July 2019 - 30 July 2019, http://dx.doi.org/10.1007/978-3-030-26369-0_9
2019
Harrison KR; Ombuki-Berman BM; Engelbrecht AP, 2019, 'The Parameter Configuration Landscape: A Case Study on Particle Swarm Optimization', in 2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings, IEEE, pp. 808 - 814, presented at 2019 IEEE Congress on Evolutionary Computation (CEC), 10 June 2019 - 13 June 2019, http://dx.doi.org/10.1109/CEC.2019.8790242
2019
Harrison KR; Engelbrecht AP; Ombuki-Berman BM, 2018, 'An adaptive particle swarm optimization algorithm based on optimal parameter regions', in 2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings, IEEE, Honolulu, HI, pp. 1 - 8, presented at IEEE Symposium Series on Computational Intelligence (IEEE SSCI), Honolulu, HI, 27 November 2017 - 01 December 2017, http://dx.doi.org/10.1109/SSCI.2017.8285342
2018
Harrison KR; Ombuki-Berman BM; Engelbrecht AP, 2018, 'Gaussian-Valued Particle Swarm Optimization', in Dorigo M; Birattari M; Blum C; Christensen AL; Reina A; Trianni V (eds.), Swarm Intelligence, Springer Verlag, Rome, Italy, pp. 368 - 377, presented at Eleventh International Conference on Swarm Intelligence (ANTS 2018), Rome, Italy, 29 October 2018 - 31 October 2018, http://dx.doi.org/10.1007/978-3-030-00533-7_31
2018
Harrison KR; Ombuki-Berman BM; Engelbrecht AP, 2017, 'Optimal parameter regions for particle swarm optimization algorithms', in 2017 IEEE Congress on Evolutionary Computation, CEC 2017 - Proceedings, IEEE, SPAIN, pp. 349 - 356, presented at IEEE Congress on Evolutionary Computation (CEC), SPAIN, 05 June 2017 - 08 June 2017, http://dx.doi.org/10.1109/CEC.2017.7969333
2017
Harrison KR; Engelbrecht AP; Ombuki-Berman BM, 2016, 'The sad state of self-adaptive particle swarm optimizers', in 2016 IEEE Congress on Evolutionary Computation, CEC 2016, IEEE, Vancouver, CANADA, pp. 431 - 439, presented at IEEE Congress on Evolutionary Computation (CEC) held as part of IEEE World Congress on Computational Intelligence (IEEE WCCI), Vancouver, CANADA, 24 July 2016 - 29 July 2016, http://dx.doi.org/10.1109/CEC.2016.7743826
2016
Harrison KR; Ombuki-Berman BM; Engelbrecht AP, 2016, 'A radius-free quantum particle swarm optimization technique for dynamic optimization problems', in 2016 IEEE Congress on Evolutionary Computation, CEC 2016, IEEE, Vancouver, CANADA, pp. 578 - 585, presented at IEEE Congress on Evolutionary Computation (CEC) held as part of IEEE World Congress on Computational Intelligence (IEEE WCCI), Vancouver, CANADA, 24 July 2016 - 29 July 2016, http://dx.doi.org/10.1109/CEC.2016.7743845
2016
Medland MR; Harrison KR; Ombuki-Berman BM, 2016, 'Automatic inference of graph models for directed complex networks using genetic programming', in 2016 IEEE Congress on Evolutionary Computation, CEC 2016, IEEE, Vancouver, CANADA, pp. 2337 - 2344, presented at IEEE Congress on Evolutionary Computation (CEC) held as part of IEEE World Congress on Computational Intelligence (IEEE WCCI), Vancouver, CANADA, 24 July 2016 - 29 July 2016, http://dx.doi.org/10.1109/CEC.2016.7744077
2016
Harrison KR; Ventresca M; Ombuki-Berman BM, 2015, 'Investigating Fitness Measures for the Automatic Construction of Graph Models', in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 189 - 200, http://dx.doi.org/10.1007/978-3-319-16549-3_16
2015
Ventresca M; Harrison KR; Ombuki-Berman BM, 2015, 'An Experimental Evaluation of Multi-Objective Evolutionary Algorithms for Detecting Critical Nodes in Complex Networks', in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 164 - 176, http://dx.doi.org/10.1007/978-3-319-16549-3_14
2015
Harrison K; Ombuki-Berman BM; Engelbrecht AP, 2015, 'The effect of probability distributions on the performance of quantum particle swarm optimization for solving dynamic optimization problems', in Proceedings - 2015 IEEE Symposium Series on Computational Intelligence, SSCI 2015, IEEE, Cape Town, SOUTH AFRICA, pp. 242 - 250, presented at IEEE Symposium Series Computational Intelligence, Cape Town, SOUTH AFRICA, 07 December 2015 - 10 December 2015, http://dx.doi.org/10.1109/SSCI.2015.44
2015
Medland MR; Harrison KR; Ombuki-Berman B, 2014, 'Incorporating expert knowledge in object-oriented genetic programming', in GECCO 2014 - Companion Publication of the 2014 Genetic and Evolutionary Computation Conference, ACM Press, pp. 145 - 146, presented at the 2014 conference companion, 12 July 2014 - 16 July 2014, http://dx.doi.org/10.1145/2598394.2598494
2014
Harrison KR; Ombuki-Berman BM; Engelbrecht AP, 2014, 'Dynamic multi-objective optimization using charged vector evaluated particle swarm optimization', in Proceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014, IEEE, Beijing, PEOPLES R CHINA, pp. 1929 - 1936, presented at IEEE Congress on Evolutionary Computation (CEC), Beijing, PEOPLES R CHINA, 06 July 2014 - 11 July 2014, http://dx.doi.org/10.1109/CEC.2014.6900399
2014
Medland MR; Harrison KR; Ombuki-Berman BM, 2014, 'Demonstrating the power of object-oriented genetic programming via the inference of graph models for complex networks', in 2014 6th World Congress on Nature and Biologically Inspired Computing, NaBIC 2014, IEEE, Porto, PORTUGAL, pp. 305 - 311, presented at 6th World Congress on Nature and Biologically Inspired Computing (NaBIC), Porto, PORTUGAL, 30 July 2014 - 01 August 2014, http://dx.doi.org/10.1109/NaBIC.2014.6921896
2014
Harrison KR; Ombuki-Berman B; Engelbrecht AP, 2013, 'Knowledge transfer strategies for vector evaluated particle swarm optimization', in Purshouse RC; Fleming PJ; Fonesca CM; Greco S; Shaw J (eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), SPRINGER-VERLAG BERLIN, Sheffield, UNITED KINGDOM, pp. 171 - 184, presented at 7th International Conference on Eevolutionary Mmulti-Criterion Optimization (EMO), Sheffield, UNITED KINGDOM, 19 March 2013 - 22 March 2013, http://dx.doi.org/10.1007/978-3-642-37140-0_16
2013
Harrison KR; Engelbrecht AP; Ombuki-Berman BM, 2013, 'A scalability study of multi-objective particle swarm optimizers', in 2013 IEEE Congress on Evolutionary Computation, CEC 2013, IEEE, Cancun, MEXICO, pp. 189 - 197, presented at IEEE Congress on Evolutionary Computation, Cancun, MEXICO, 20 June 2013 - 23 June 2013, http://dx.doi.org/10.1109/CEC.2013.6557570
2013
Journal articles
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Harrison KR; Elsayed S; Garanovich IL; Weir T; Galister M; Boswell S; Taylor R; Sarker R, 2021, 'A Hybrid Multi-Population Approach to the Project Portfolio Selection and Scheduling Problem for Future Force Design', IEEE Access, vol. 9, pp. 83410 - 83430, http://dx.doi.org/10.1109/ACCESS.2021.3086070
2021
Harrison KR; Bidgoli AA; Rahnamayan S; Deb K, 2021, 'Image-based benchmarking and visualization for large-scale global optimization', Applied Intelligence, http://dx.doi.org/10.1007/s10489-021-02637-3
2021
Harrison KR; Elsayed S; Garanovich I; Weir T; Galister M; Boswell S; Taylor R; Sarker R, 2020, 'Portfolio Optimization for Defence Applications', IEEE Access, vol. 8, pp. 60152 - 60178, http://dx.doi.org/10.1109/ACCESS.2020.2983141
2020
Harrison KR; Ombuki-Berman BM; Engelbrecht AP, 2019, 'A parameter-free particle swarm optimization algorithm using performance classifiers', Information Sciences, vol. 503, pp. 381 - 400, http://dx.doi.org/10.1016/j.ins.2019.07.016
2019
Ventresca M; Harrison KR; Ombuki-Berman BM, 2018, 'The bi-objective critical node detection problem', European Journal of Operational Research, vol. 265, pp. 895 - 908, http://dx.doi.org/10.1016/j.ejor.2017.08.053
2018
Harrison KR; Engelbrecht AP; Ombuki-Berman BM, 2018, 'Optimal parameter regions and the time-dependence of control parameter values for the particle swarm optimization algorithm', Swarm and Evolutionary Computation, vol. 41, pp. 20 - 35, http://dx.doi.org/10.1016/j.swevo.2018.01.006
2018
Harrison KR; Engelbrecht AP; Ombuki-Berman BM, 2018, 'Self-adaptive particle swarm optimization: a review and analysis of convergence', Swarm Intelligence, vol. 12, pp. 187 - 226, http://dx.doi.org/10.1007/s11721-017-0150-9
2018
Harrison KR; Engelbrecht AP; Ombuki-Berman BM, 2016, 'Inertia weight control strategies for particle swarm optimization: Too much momentum, not enough analysis', Swarm Intelligence, vol. 10, pp. 267 - 305, http://dx.doi.org/10.1007/s11721-016-0128-z
2016
Harrison KR; Ventresca M; Ombuki-Berman BM, 2016, 'A meta-analysis of centrality measures for comparing and generating complex network models', Journal of Computational Science, vol. 17, pp. 205 - 215, http://dx.doi.org/10.1016/j.jocs.2015.09.011
2016

Governor General's Academic Gold Medal, Brock University, 2015.

Distinguished Graduate Student Award in Computer Science, Brock University, 2015.

Organisational units
lensSchool of Engineering & Information Technology