Prof. Beatrice OmbukiBerman
Submitted/Under revision
Refereed Publications (Journals, Conference Proceedings and Book
Chapter)
 Optimizing ScaleFree Network Robustness with the Great Deluge Algorithm
J. Paterson and B. M. OmbukiBerman
The 31st International Conference on Industrial, Engineering & Other applications of Applied
Intelligent Systems,
IEAAIE 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. OmbukiBerman
Swarm and Evolutionary Computation , Elsevier, In Press, Available online January 2018.
 Merging and Decomposition Variants of Cooperative Particle
Swarm Optimization:
New Algorithms for Large Scale Optimization Problems
Jay Douglas, Andries Engelbrecht and Beatrice OmbukiBerman
2018 2nd International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence ,
ISMSI2018, Accepted Phuket, Thailand, March 2018.
 A BiObjective Critical Node Detection Problem
M. Venntresca, K. Harrison, and B.M. OmbukiBerman
European Journal of Operational Research, 254(3):895908, March 2018.
 A Scalability Study of ManyObjective Optimization Algorithms
Justin Maltese, Beatrice M. OmbukiBerman, and Andries P. Engelbrecht.
IEEE Transactions on Evolutionary Computation, pp: 7996, February 2018.
 SelfAdaptive Particle Swarm Optimization: A review and Analysis of Convergence
K.R Harrison, A.P Engelbrecht, and B. M OmbukiBerman
Swarm Intelligence , Springer , In Press, Available online, November 2017.
 An Age Layered Population Structure Genetic Algorithm for MultiDepot Vehicle
Routing
Audrey OpokuAmankwaar and B.M Ombuki
2017 IEEE Symposium Series on Computation Intelligence , pp.34033410, Hawai, November 2017.
 Optimal Parameter Regions for Particle Swarm Optimization Algorithms
Kyle R. Harrison Beatrice M. OmbukiBerman, and Andries P. Engelbrecht.
IEEE Congress on Evolutionary Computation, CEC 2017 pp. 349356, Spain, June 2017.
 Inertia weight control strategies for particle swarm optimization
Kyle R. Harrison, Andries P. Engelbrecht and Beatrice M. OmbukiBerman.
Swarm Intelligence , Volume 10, Issue 4, pp:267305, December 2016.
 A MetaAnalysis of Centrality Measures for Comparing and Generating Complex Network Models.
Kyle Robert Harrison, Mario Ventresca, and Beatrice M. OmbukiBerman.
Journal of Computational
Science, Elsevier, 17(1):205215, November 2016.
 Automatic Inference of Graph
Models for Directed Complex Networks using Genetic Programming
Michael Medland, Kyle Robert Harrison, and Beatrice M. OmbukiBerman.
IEEE Congress on Evolutionary Computation, IEEE CEC 2016, pp. 23372344, Vancouver, July 2016.
 ParetoBased ManyObjective Optimization using
Knee Points
Justin Maltese, Beatrice M. OmbukiBerman, and Andries P. Engelbrecht.
IEEE Congress on Evolutionary Computation, IEEE CEC 2016, pp. pp. 3678  3686, Vancouver, July 2016.
 The Sad State of SelfAdaptive Particle Swarm Optimizers
Kyle R. Harrison, Andries P. Engelbrecht and Beatrice M. OmbukiBerman.
IEEE Congress on Evolutionary Computation, IEEE CEC 2016, pp. 431439, Vancouver, July 2016.
 A RadiusFree Quantum Particle Swarm Optimization Technique for Dynamic Optimization Problems
K.R. Harrison, B.M OmmbukiBerman, and Andries P. Engelbrecht.
IEEE Congress on Evolutionary Computation, IEEE CEC 2016, pp. 578585, Vancouver, July 2016.
 HighDimensional Multiobjective Optimization Using Cooperative VectorEvaluated
Particle Swarm Optimization With Random Variable Grouping.
Justin Maltese, Andries P. Engelbrecht and Beatrice M. Ombuki.
2015 IEEE Symposium on
Swarm Intelligence. 1302  1309, Cape Town, SA, December 2015.
 The Effect of Probability Distributions on the Performance of Quantum Particle Swarm
Optimization for Solving Dynamic Optimization Problems.
Kyle Harrison, Beatrice OmbukiBerman and Andries P. Engelbrecht.
2015 IEEE Symposium on
Swarm Intelligence. 242  250, Cape Town, SA, December 2015.
 Cooperative Vector Evaluated Particle Swarm Optimization for Multiobjective Optimization.
Justin Maltese, Beatrice OmbukiBerman and Andries P. Engelbrecht.
2015 IEEE Symposium on
Swarm Intelligence. pp. 1294  1301, Cape Town, SA, December 2015.
 A GA Approach for finding decision rules based on bireducts
Oleg Rybik, Ivo Duntsch and Beatrice OmbukiBerman
Extended abstract, RST 2015, Warsaw, Poland, June 2015.
 Evaluating Landscape Characteristics of Dynamic Benchmark Functions.
Ron Bond, Andries Engelbrecht and Beatrice OmbukiBerman.
2015 IEEE Congress on Evolutionary
Computation, CEC 2015, (CEC 2015), pp. 187  195, Sendai, Japan, May 2015.
 VectorEvaluated Particle Swarm Optimization with Local Search.
Derek Dibblee, Justin Maltese, Beatrice OmbukiBerman and Andries Engelbrecht.
2015 IEEE Congress on Evolutionary
Computation, CEC 2015, (CEC 2015), pp. 1343  1350, Sendai, Japan, May 2015.
 Investigating Fitness Measures for the Automatic Construction of Graph Models.
Kyle Harrison, Mario Ventresca and Beatrice OmbukiBerman.
Lecture Notes in Computer Science , 9028 pp:189200, 2015
18th European Conference on the Applications of Evolutionary Computation
, EvoCOMPLEX, Copenhagen, Denmark, April, 2015.
 An Experimental Evaluation of MultiObjective
Evolutionary Algorithms for Detecting Critical
Nodes in Complex Networks.
Mario Ventresca, Kyle Harrison, and Beatrice OmbukiBerman.
Lecture Notes in Computer Science , 9028 pp:164176,2015.
18th European Conference on the Applications of Evolutionary Computation
, EvoCOMPLEX, Copenhagen, Denmark, April 2015.
 Demonstrating the Power of ObjectOriented Genetic Programming via the Inference of Graph Models for
Complex Networks.
Michael Medland, Kyle Harrison and Beatrice OmbukiBerman.
6th World Congress on Nature and Biologically Inspired Computing, (NABIC 2014), pp.
pp. 305311, Portugal, August 2014.
 Dynamic MultiObjective Optimization using Charged Vector Evaluated Particle Swarm Optimization.
Kyle Harrison, Beatrice OmbukiBerman and Andries Engelbrecht.
IEEE Conference on Evolutionary Computation, (CEC 2014), pp. 1929  1936, Beijing, China, July 2014.
 Incorporating Expert Knowledge in ObjectOriented Genetic Programming.
Michael
Medland, Kyle Harrison and Beatrice OmbukiBerman.
Genetic and Evolutionary Computation Conference,
GECCO 2014, pp. 145  146, Vancouver, July 2014.
 Genetic Programming for the Automatic Inference of Graph Models for Complex Networks.
A. Bailey, M. Ventresca and B.OmbukiBerman.
IEEE Transactions on Evolutionary Computation , 18(3):405419, June, 2014.
 Automatic Inference of Hierarchical Graph Models using Genetic Programming
with an Application to Cortical Networks.
A. Bailey, B. OmbukiBerman and M. Ventresca.
Genetic and
Evolutionary
Computation Conference, GECCO 2013 , pp.893900,
Amsterdam, July 2013.
 A Scalability Study of MultiObjective Particle Swarm Optimizers.
K. Harrison, A.P. Engelbrecht and B.OmbukiBerman
2013 IEEE
Conference on Evolutionary Computation (CEC 2013), , pp.189197, Mexico,
June
2013.
 Cooperative Particle Swarm Optimization for Dynamic Environments.
N. Unger, B. OmbukiBerman
and A. P. Engelbrecht.
2013 IEEE Symposium on
Swarm Intelligence. pp. 172179, Singapore, April 2013.
 Discrete Particle Swarm Optimization for the Single Allocation Hub Location Problem.
A. Bailey, B. OmbukiBerman and S. Aseibola
2013 IEEE Symposium
Series on Computational Intelligence. pp.9298, Singapore, April 2013.

Predicting GAs Performance on the Vehicle Routing Problem Using Information Theoretic Landscape Measures.
,
M. Ventresca, B. OmbukiBerman and A. Runka.
13th European Conference on Evolutionary Computation in Combinatorial Optimisation (EvoCOP), pp.
214225, Vienna, Austria, April 2013.

Knowledge Transfer Strategies for Vector Evaluated Particle Swarm Optimization.
K. Harrison, B. OmbukiBerman and A. Engelbrecht.
Evolutionary MultiCriterion Optimization, Lecture Notes in Computer Science Volume 7811, 2013, pp. 171184 , March 2013.

Automatic Generation of Graph Models for Complex Networks using Genetic Programming,
A. Bailey, M. Ventresca, and B. OmbukiBerman.
Genetic and Evolutionary Computation Conference, GECCO 2012 ,
pp.711718, Philadelphia, USA, July 2012.
 Using Summed Ranks and Pareto Ranking to Design Outpatient Schedules,
OmbukiBerman, B., Klassen, K. and Harrington, A.
Canadian Operational Research Society Conference , Niagara Falls, Ontario, June, 2012.
 Developing Appointment Schedules with Genetic Algorithms,
Klassen, K., OmbukiBerman, B. and Harrington, A..
Production and Operations Management Society Conference , Chicago, Illinois, April, 2012.
 An Efficient Genetic Algorithm for the Uncapacitated Single Allocation Hub Location Problem.
M. Naeem and B. OmbukiBerman.
2010 IEEE Conference on Evolutionary Computation (CEC 2010) , pp.941948,
Barcelona, Spain, July 1823, 2010.

A Search Space Analysis for the Waste Collection Vehicle Routing Problem with Time Windows.
A. Runka, B. OmbukiBerman, M. Ventresca.
Refereed Poster
Paper, GECCO 2009 , Montreal,
July 2009.
 Using genetic algorithms for multidepot vehicle routing.
B. OmbukiBerman and F. Hanshar.
F.B. Pereira, J.Tavares (Eds.)
in, BioInspired Algorithms for the Vehicle
Routing Problem, SpringerStudies in
Computational Intelligence, v.161, pp:7799, 2009.
 Genetic Algorithm Cryptanalysis of Substitution Permutation Network.
J. Brown, S. Houghten and B.
OmbukiBerman.
IEEE Symposium on Computational
Intelligence in Cyber Security . pp:115121, Nashville, Tennessee, USA, 2009.

Particle swarm optimization for the Design of Two Connected Networks with Bounded Rings.
E. B. Foxwell and B. OmbukiBerman.
Journal of High Performance System Architecture , Vol. 1. no.4, pp. 220230, 2008.
(Initial results presented at Workshop on Parallel
Architectures and Bioinspired Algorithms,
pp:2129, Toronto, Oct. 2008)
 Dynamic Vehicle Routing
using Genetic Algorithm.
F.T. Hanshar and B. OmbukiBerman.
Applied Intelligence, 27(1):8999, August 2007.
 Waste collection
vehicle routing problem with time windows using multiobjective genetic algorithms .
B. OmbukiBerman, A. Runka and F. Hanshar.
Computational Intelligence (CI 2007) , Banff, Canada, July 2007.
 Search Difficulty of TwoConnected
Ringbased Topological Network Designs.
B. OmbukiBerman, M. Ventresca.
IEEE Symposium on Foundations of Computational Intelligence
(FOCI), pp:267274, Honolulu, USA, April 2007.
 Epistasis in MultiObjective
Evolutionary Recurrent NeuroControllers.
M. Ventresca, B. OmbukiBerman.
IEEE Symposium on Artificial Life (CIALIFE),
pp:7784, Honolulu, USA, April 2007.
 Search Space Analysis of Recurrent Spiking and Continuoustime Neural
Networks.
M. Ventresca and B. M. Ombuki.
IEEE International Joint Conference on Neural Networks (IJCNN), pp:89478954, Vancouver, Canada, July 2006.
 MultiObjective Genetic Algorithms for Vehicle Routing
Problems with
Time Windows. B. Ombuki, Brian J. Ross and F. Hanshar.
Applied Intelligence, 24(1):1730, February 2006.
 A Genetic Algorithm for the
Design of Two Connected Networks with Bounded
Rings". M. Ventresca and B. Ombuki.
Computational Intelligence
and Applications, Special Issue on NatureInspired Approaches
to Networks and Telecommunications, 5(2):267281, November 2005.
 Ant Colony Optimization for Job Shop
Scheduling Problem.
M. Ventresca and B. M. Ombuki.
Proceedings of 8th IASTED Intl. Conf. On
Artificial Intelligence and Soft Computing, (ASC 2004), CDROM.
451152. Marbella,Spain, ed. A.P.del Pobil, ACTA Press, September
2004.
 A Genetic Algorithm for
the Design of Two Connected Networks with Bounded
Rings". M. Ventresca and B. Ombuki.
Workshop on Nature Inspired Approaches to
Networks and Telecommunications
workshop at the 8th International Conference on Parallel Problem
Solving from Nature (PPSN VIII) ,
Birmingham, UK, on 1822
September, 2004.

Local Search Genetic Algorithm for the Job Shop Scheduling Problem.
B. Ombuki and M. Ventresca.
Applied Intelligence 21 (1): 99109, July 2004.
 Metaheuristics for the Job Shop Scheduling Problem.
M. Ventresca and B. Ombuki. Proceedings of Late
Breaking Papers, Genetic and Evolutionary Computation Conference M. Ventresca and B.M. Ombuki.
(GECCO2003) pp.303306,
Chicago, 2003.

A Hybrid Search Based on Genetic Algorithms and Tabu Search for
Vehicle Routing. B.Ombuki, M. Nakamura & M.Osamu.
6th International Conference on
Artificial Intelligence and Soft
Computing , pp. 176181,
Banff, Canada, July 2002.

An Evolutionary Algorithm Approach to the Design of
Minimum
Cost Survivable Networks with Bounded Rings. B.Ombuki, M. Nakamura & M.Osamu.
IEICE Transactions on Fundamentals of
Electronics, Communications
and Computer Sciences, Vol.E84A No.6 pp.15451548, June 2001.
 Cyclic jobshopscheduling based
on
evolutionary Petri nets. Nakamura, M., Tome, H., Hachiman, K., Ombuki,
B.M. and Onaga, K.
Industrial Electronics Society, 2000. 26th
Annual Conference of the IEEE,
IECON 2000 , Vol. 4, pp.
28552860, 2000, Nagoya, Japan.
 A
Genetic Algorithm Approach for the Design of
Minimum Cost
Survivable Networks With Bounded Rings. B. Ombuki, M. Nakamura, Z. Nakao and K. Onaga.
Proceedings of
International Technical Conference On
Circuits/Systems, Computers And Communications, ITCCSSS'00 , Vol.
1,pp 493496, 2000, Pusan, Korea.

A Flexible Routing based on Object Oriented GAs in Vehicle Routing
Problem with Time Constraints.
Khan S., Nakamura M., Ombuki B.M and Onaga K.
Proceedings of the IEICE General Conference (Institute of Electronics, Information
and Communication Engineers) ,
VOL.2000;NO.;PAGE.252(2000).

Evolutionary Computation for Topological Optimization
of
3Connected Computer Networks. B. Ombuki, M. Nakamura, Z. Nakao and K.
Onaga.IEEE International
Conference on Systems, Man, and
Cybernetics , CD, Tokyo, Oct.
1999

A Genetic Algorithm Approach to Vehicle Routing
Problem with
Time Deadlines in Geographical Information Systems. O.Maeda, M.Nakamura, B. Ombuki, and K.
Onaga. IEEE
International Conference on Systems, Man, and Cybernetics,
Vol.II, pp.595600, Tokyo, Oct.
1999.

Experimental Evaluation of an Evolutionary Scheduling Scheme
based
on gkGA Approach to the Job Shop Problem. B. Ombuki, M. Nakamura, and K. Onaga.
Second
International Conference on
KnowledgeBased Intelligent
Electronic Systems , Vol. 3, pp 197201, Adlaide, Australia, April 1998.

An Evolutionary Scheduling Scheme based on gkGA Approach to
the Job
Shop Problem. B. Ombuki, M. Nakamura, and K. Onaga.
IEICE Transactions on Fundamentals of
Electronics, Communication and
Computer Sciences, Vol. E pp
10631071, June 1998.

A New Hybrid GA Solution to Combinatorial
Optimization Problems  An
Application to the Multiprocessor Scheduling Problems. . Nakamura, B. Ombuki, K. Shimabukuro, and K.
Onaga.
Journal for Artificial
Life and
Robotics,
Springer Verlag, Vol.2, 7479, 1998.
 A Hybridized GA Approach to the Job Shop Problem. B. Ombuki, M. Nakamura, and K. Onaga.
Proceedings of International Technical Conference On Circuits/Systems,
Computers
and
Communications, ITCCSSS'97, Vol. 1,pp 483486, Okinawa, Japan.
Theses
 ( PhD ) Evolutionary Computation for Scheduling
and Network Design Problems
 ( ME ) GenetizedKnowledge Genetic Algorithm Approach
for Combinatorial Optimization