• Ph.D., Intelligent Systems Engineering, University of the Ryukyus, Japan.
• ME., Information Engineering, Faculty of Engineering, University of the Ryukyus, Japan.
• Certificate, Japanese Language Studies, Osaka University of Foreign Studies, Japan.
• BSc., Double (Mathematics and Statistics) & Computer Science (minor), Jomo Kenyatta University of Agriculture and Technology, Kenya.

RECENT PUBLICATIONS

• Multi-guide Particle Swarm Optimization Archive Management Strategies for Dynamic Optimization Problems
  Jocko Pawel, Beatrice M. Ombuki-Berman and Andries P Engelbrecht
  Swarm Intelligence (Springer), Accepted, January, 2022.
• An analysis of the impact of subsampling on the neural network error surface
  Cody Dennis, Andries Engelbrecht and Beatrice M. Ombuki-Berman
  Neurocomputing (Elsevier), 466:252-264, November 2021.
• Visualizing and characterizing the parameter configuration landscape of Particle Swarm Optimization using physical landform classification
  K. R. Harrison, B. M. Ombuki-Berman and A. P. Engelbrecht
  2021 IEEE CEC Congress of Evolutionary Computation , CEC 2021, pp.2299-2306, Krakow, Poland, June 2021.
• Decision Space Scalability Analysis of Multi-objective Particle Swarm Optimization Algorithms
  A. Madani, B. M. Ombuki-Berman and A.P Engelbrecht
  2021 IEEE CEC Congress of Evolutionary Computation , CEC 2021, pp.2179-2186, Krakow, Poland, June 2021.
• Predicting particle swarm control parameters from fitness landscape characteristics
  C. Dennis, B. M. Ombuki-Berman and A.P Engelbrecht
  2021 IEEE CEC Congress of Evolutionary Computation , CEC 2021, pp.2289-2298, Krakow, Poland, June 2021.
• T. Crane, A. P. Engelbrecht and B. M. Ombuki-Berman
  12th International Conference on Swarm Intelligence (ICSI’21), Qingdao, China, to Appear in Springer-Nature Lecture Notes (LNCS) in Computer Scienc, Accepted MArch 2021.
• Visualizing and characterizing the parameter configuration landscape of differential evolution using physical landform classification
  K. R. Harrison, B. M. Ombuki-Berman and A. P. Engelbrecht
  2020 IEEE Symposium Series on Computational Intelligence , IEEE, Canberra, Australia, pp.2437- 2444, Devember 2020.
• NichePSO and the Merging Subswarm Problem
  T. Crane, B. Ombuki-Berman and A. Engelbrecht
  2020 7th International Conference on Soft Computing & Machine Intelligence (ISCMI) , Stockholm, Sweden,pp. 17-22, November 2020.
• A Hybrid Approach to Network Robustness optimization using edge rewiring and edge Addition
  J. Paterson and B. M. Ombuki-Berman 2020 IEEE International Conference on Systems, Man and Cybernetics , IEEE SMC 2020, Toronto, pp. 4051- 4057, October 2020.
• Swarm Based Algorithms for Neural Network Training
  R. McLean, B. M. Ombuki-Berman and A.P Engelbrecht
  2020 IEEE International Conference on Systems, Man and Cybernetics IEEE SMC 2020, Toronto, pp. 2585- 2592, October 2020.
• An Analysis of Activation Function Saturation in Particle Swarm Optimization Trained Neural Networks Training
  C. Dennis, A.P Engelbrecht and B. M. Ombuki-Berman
  Neural Processing Letters , 52:1123-1153, September 2020.
• Juan C. Burguillo: Self-organizing coalitions for managing complexity
  Ombuki-Berman B.
  Genetic Programming and Evolvable Machines (2020).
• https://doi.org/10.1007/s10710-019-09372-2. Invited Book Review.
  Random Regrouping and Factorization in Cooperative Particle Swarm Optimization Based Large-Scale Neural Network Training
  Cody Dennis, Beatrice M. Ombuki-Berman, and Andries P. Engelbrecht
  Neural Processing Letters 51(1), 759-796, 2020 , DOI 10.1007/s11063-019-10112-x
• A Parameter-Free Particle Swarm Optimization Algorithm using Performance Classifiers
  K.R.Harrison,B.M.Ombuki-Berman,and A.P.Engelbrecht
  Information Sciences vol. 503, pp.381- 400, 2019.
• The Parameter Configuration Landscape: A Case Study on Particle Swarm Optimization
  K. R. Harrison, B. M. Ombuki-Berman, and A. P. Engelbrecht
  IEEE Congress on Evolutionary Computation (CEC 2019) , pp. 808-814, 2019.
• An Analysis of Control Parameter Importance in the Particle Swarm Optimization Algorithm
  K.R.Harrison, B.M.Ombuki-Berman, and A.P.Engelbrecht
  In Advances in Swarm Intelligence, , Y. Tan, Y. Shi, and B. Niu, Eds., Springer International Publishing, pp. pp. 93-105, 2019.
• Optimizing Scale-Free Network Robustness with the Great Deluge Algorithm
  J. Paterson and B. M. Ombuki-Berman
  The 31st International Conference on Industrial, Engineering & Other applications of Applied Intelligent Systems, IEA-AIE 2018 , Accepted, Montreal, June 2018.
• Optimal Parameter Regions and the Time Dependence of Control Parameter Values
  for the Particle Swarm Optimization Algorithm
  K.R Harrison, A.P Engelbrecht and B. M. Ombuki-Berman
  Swarm and Evolutionary Computation 41 , pp. 20-35, Elsevier, 2018.
• Merging and Decomposition Variants of Cooperative Particle Swarm Optimization:
  New Algorithms for Large Scale Optimization Problems
  Jay Douglas, Andries Engelbrecht and Beatrice Ombuki-Berman
  2018 2nd International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence ,
  ISMSI2018, Accepted Phuket, Thailand, March 2018.
• Gaussian-Valued Particle Swarm Optimization
  K.R Harrison, B. M Ombuki-Berman and A.P Engelbrecht
  in Swarm Intelligence , M. Dorigo, M. Birattari, C. Blum, A.L Christensen, A. Reina, and V. Trianni, Eds, Springer International Publishing, pp. 368- 377, 2018.
• A Bi-Objective Critical Node Detection Problem
  M. Ventresca, K. Harrison, and B.M. Ombuki-Berman
  European Journal of Operational Research, 254(3):895-908, March 2018.
• A Scalability Study of Many-Objective Optimization Algorithms
  Justin Maltese, Beatrice M. Ombuki-Berman, and Andries P. Engelbrecht.
  IEEE Transactions on Evolutionary Computation, pp: 79-96, February 2018.
• Self-Adaptive Particle Swarm Optimization: A review and Analysis of Convergence
  K.R Harrison, A.P Engelbrecht, and B. M Ombuki-Berman
  Swarm Intelligence , 12(3) pp. 187–226, Springer, 2018.
• An Age Layered Population Structure Genetic Algorithm for Multi-Depot Vehicle Routing
  Audrey Opoku-Amankwaar and B.M Ombuki
  2017 IEEE Symposium Series on Computation Intelligence , pp.3403-3410, Hawai, November 2017.
• Optimal Parameter Regions for Particle Swarm Optimization Algorithms
  Kyle R. Harrison Beatrice M. Ombuki-Berman, and Andries P. Engelbrecht.
  IEEE Congress on Evolutionary Computation, CEC 2017 pp. 349-356, Spain, June 2017.
• Inertia weight control strategies for particle swarm optimization
  Kyle R. Harrison, Andries P. Engelbrecht and Beatrice M. Ombuki-Berman.
  Swarm Intelligence , Volume 10, Issue 4, pp:267-305, December 2016.
• A Meta-Analysis of Centrality Measures for Comparing and Generating Complex Network Models.
  Kyle Robert Harrison, Mario Ventresca, and Beatrice M. Ombuki-Berman.
  Journal of Computational Science, Elsevier, 17(1):205-215, November 2016.
• Automatic Inference of Graph Models for Directed Complex Networks using Genetic Programming
  Michael Medland, Kyle Robert Harrison, and Beatrice M. Ombuki-Berman.
  IEEE Congress on Evolutionary Computation, IEEE CEC 2016, pp. 2337-2344, Vancouver, July 2016.
• Pareto-Based Many-Objective Optimization using Knee Points
  Justin Maltese, Beatrice M. Ombuki-Berman, and Andries P. Engelbrecht.
  IEEE Congress on Evolutionary Computation, IEEE CEC 2016, pp. pp. 3678 – 3686, Vancouver, July 2016.
• The Sad State of Self-Adaptive Particle Swarm Optimizers
  Kyle R. Harrison, Andries P. Engelbrecht and Beatrice M. Ombuki-Berman.
  IEEE Congress on Evolutionary Computation, IEEE CEC 2016, pp. 431-439, Vancouver, July 2016.
• A Radius-Free Quantum Particle Swarm Optimization Technique for Dynamic Optimization Problems
  K.R. Harrison, B.M Ommbuki-Berman, and Andries P. Engelbrecht.
  IEEE Congress on Evolutionary Computation, IEEE CEC 2016, pp. 578-585, Vancouver, July 2016.