Christopher W Cleghorn

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Andries EngelbrechtStellenbosch University, Department of Industrial Engineering, and Division of Computer ScienceVerified email at sun.ac.za
Elre OldewageUniversity of CambridgeVerified email at cam.ac.uk
Steven JamesUniversity of the WitwatersrandVerified email at wits.ac.za
Michael BeukmanPhD Student, University of OxfordVerified email at eng.ox.ac.uk
Muhammad Umair NasirPhD Student at New York University and University of the WitwatersrandVerified email at nyu.edu
Stefan Van der StocktIBMVerified email at ibm.com
Barend LeonardUniversity of PretoriaVerified email at cs.up.ac.za
Julian TogeliusAssociate Professor of Computer Science and Engineering, New York University; co-founder, modl.aiVerified email at nyu.edu
Sam EarleNew York University Tandon School of EngineeringVerified email at nyu.edu
Anna Sergeevna BosmanSenior Lecturer, University of PretoriaVerified email at cs.up.ac.za
Gabriela OchoaProfessor of Computer Science, University of StirlingVerified email at cs.stir.ac.uk
Katherine M. MalanDepartment of Decision Sciences, UNISAVerified email at ieee.org
Renate ThiedeLecturer in the Department of Statistics, University of PretoriaVerified email at up.ac.za
Inger Fabris-RotelliUniversity of PretoriaVerified email at up.ac.za
Pravesh DebbaCSIR, Built Environment, Spatial Planning and SystemsVerified email at csir.co.za
Willem S. van HeerdenUniversity of PretoriaVerified email at cs.up.ac.za
Kshitij ThoratUniversity of Pretoria, South AfricaVerified email at up.ac.za
Duncan TilleyPostgraduate Student, University of PretoriaVerified email at tuks.co.za
Roger DeaneUniversity of the WitwatersrandVerified email at wits.ac.za
Rethabile MabasoMSc Candidate, Computer Science, University of PretoriaVerified email at tuks.co.za

Christopher W Cleghorn

Applied Science Manager (AWS) & Visiting Associate Professor, University of the Witwatersrand

Verified email at wits.ac.za

Swarm Intelligence Machine Learning


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A generalized theoretical deterministic particle swarm model CW Cleghorn, AP Engelbrecht Swarm intelligence 8, 35-59, 2014	109	2014
Particle swarm stability: a theoretical extension using the non-stagnate distribution assumption CW Cleghorn, AP Engelbrecht Swarm Intelligence 12, 1-22, 2018	91	2018
Particle swarm convergence: An empirical investigation CW Cleghorn, AP Engelbrecht 2014 IEEE Congress on Evolutionary Computation (CEC), 2524-2530, 2014	76	2014
Particle swarm variants: standardized convergence analysis CW Cleghorn, AP Engelbrecht Swarm Intelligence 9, 177-203, 2015	66	2015
The merits of velocity clamping particle swarm optimisation in high dimensional spaces ET Oldewage, AP Engelbrecht, CW Cleghorn 2017 IEEE Symposium Series on Computational Intelligence (SSCI), 1-8, 2017	38	2017
Multi-guide particle swarm optimization for multi-objective optimization: empirical and stability analysis C Scheepers, AP Engelbrecht, CW Cleghorn Swarm Intelligence 13 (3), 245-276, 2019	35	2019
Particle Swarm Optimizer: The Impact of Unstable Particles on Performance CW Cleghorn, AP Engelbrecht Computational Intelligence, 2016 IEEE Symposium Series on, 2016	35	2016
Particle swarm convergence: Standardized analysis and topological influence CW Cleghorn, AP Engelbrecht International Conference on Swarm Intelligence, 134-145, 2014	34	2014
Critical considerations on angle modulated particle swarm optimisers BJ Leonard, AP Engelbrecht, CW Cleghorn Swarm Intelligence 9, 291-314, 2015	31	2015
LLMatic: neural architecture search via large language models and quality diversity optimization MU Nasir, S Earle, J Togelius, S James, C Cleghorn Proceedings of the Genetic and Evolutionary Computation Conference, 1110-1118, 2024	29	2024
Movement patterns of a particle swarm in high dimensional spaces ET Oldewage, AP Engelbrecht, CW Cleghorn Information Sciences 512, 1043-1062, 2020	28	2020
Fully informed particle swarm optimizer: Convergence analysis CW Cleghorn, AP Engelbrecht Evolutionary Computation (CEC), 2015 IEEE Congress on, 164-170, 2015	27	2015
Particle swarm optimization: stability analysis using n-informers under arbitrary coefficient distributions CW Cleghorn, B Stapelberg Swarm and Evolutionary Computation 71, 101060, 2022	17	2022
Procedural Content Generation using Neuroevolution and Novelty Search for Diverse Video Game Levels M Beukman, CW Cleghorn, S James Proceedings of the Genetic and Evolutionary Computation Conference, 1028–1037, 2022	16	2022
Degrees of stochasticity in particle swarm optimization ET Oldewage, AP Engelbrecht, CW Cleghorn Swarm intelligence 13, 193-215, 2019	15	2019
Fitness-distance-ratio particle swarm optimization: stability analysis CW Cleghorn, AP Engelbrecht Proceedings of the genetic and evolutionary computation conference, 12-18, 2017	14	2017
Understanding parameter spaces using local optima networks: a case study on particle swarm optimization CW Cleghorn, G Ochoa Proceedings of the Genetic and Evolutionary Computation Conference Companion …, 2021	13	2021
Boundary constraint handling techniques for particle swarm optimization in high dimensional problem spaces ET Oldewage, AP Engelbrecht, CW Cleghorn Swarm Intelligence: 11th International Conference, ANTS 2018, Rome, Italy …, 2018	13	2018
Recent advances in particle swarm optimization analysis and understanding AP Engelbrecht, CW Cleghorn Proceedings of the 2020 Genetic and Evolutionary Computation Conference …, 2020	11	2020
Particle swarm optimization: Understanding order-2 stability guarantees CW Cleghorn Applications of Evolutionary Computation: 22nd International Conference …, 2019	11	2019

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