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Noha Mohamed Hamza
Noha Mohamed Hamza
Research Associate , University of New South Wales - Australia, Canberra
Verified email at unsw.edu.au
Title
Cited by
Cited by
Year
Testing united multi-operator evolutionary algorithms on the CEC2014 real-parameter numerical optimization
SM Elsayed, RA Sarker, DL Essam, NM Hamza
2014 IEEE congress on evolutionary computation (CEC), 1650-1657, 2014
802014
Constraint consensus mutation-based differential evolution for constrained optimization
NM Hamza, DL Essam, RA Sarker
IEEE Transactions on Evolutionary Computation 20 (3), 447-459, 2015
712015
Testing united multi-operator evolutionary algorithms-II on single objective optimization problems
S Elsayed, N Hamza, R Sarker
2016 IEEE congress on evolutionary computation (CEC), 2966-2973, 2016
682016
A constraint consensus memetic algorithm for solving constrained optimization problems
NM Hamza, RA Sarker, DL Essam, K Deb, SM Elsayed
Engineering Optimization 46 (11), 1447-1464, 2014
182014
Differential evolution combined with constraint consensus for constrained optimization
NM Hamza, SM Elsayed, DL Essam, RA Sarker
2011 IEEE Congress of evolutionary computation (CEC), 865-872, 2011
182011
Differential evolution with multi-constraint consensus methods for constrained optimization
NM Hamza, RA Sarker, DL Essam
Journal of Global Optimization 57, 583-611, 2013
152013
Differential evolution with a mix of Constraint Consenus methods for solving a real-world Optimization Problem
NM Hamza, RA Sarker, DL Essam
2012 IEEE Congress on Evolutionary Computation, 1-7, 2012
112012
Sensitivity-based change detection for dynamic constrained optimization
N Hamza, R Sarker, D Essam
IEEE Access 8, 103900-103912, 2020
52020
Evolutionary approach for dynamic constrained optimization problems
N Hamza, R Sarker, D Essam, S Elsayed
Alexandria Engineering Journal 66, 827-843, 2023
32023
Evolutionary search from the interior of feasible space
N Hamza, R Sarker, D Essam
2020 IEEE Symposium Series on Computational Intelligence (SSCI), 353-359, 2020
32020
Enhancing evolutionary algorithms by efficient population initialization for constrained problems
S Elsayed, R Sarker, N Hamza, CAC Coello, E Mezura-Montes
2020 IEEE Congress on Evolutionary Computation (CEC), 1-8, 2020
32020
Differential evolution with a constraint consensus mutation for solving optimization problems
NM Hamza, DL Essam, RA Sarker
2014 IEEE Congress on Evolutionary Computation (CEC), 991-997, 2014
32014
Exploring the feasible space using constraint consensus in solving constrained optimization problems
NM Hamza, DL Essam, RA Sarker
Artificial Life and Computational Intelligence: Second Australasian …, 2016
22016
Evolutionary constrained optimization with dynamic changes and uncertainty in the objective function
N Hamza, S Elsayed, R Sarker, D Essam
2022 14th International Conference on Software, Knowledge, Information …, 2022
12022
Hybridizing constraint consensus methods with evolutionary algorithms for constrained optimization
N Hamza
UNSW Sydney, 2012
12012
Constraint Consensus for Solving Large-scale Constrained Optimization Problems
N Hamza, R Sarker, D Essam, S Elsayed
2024 IEEE Congress on Evolutionary Computation (CEC), 1-8, 2024
2024
Solving constrained problems with dynamic objective functions
N Hamza, S Elsayed, R Sarker, D Essam
2022 IEEE Congress on Evolutionary Computation (CEC), 1-8, 2022
2022
Search Techniques for Evolutionary Constrained Optimization
N Hamza
UNSW Sydney, 2016
2016
Memetic Differential Evolution combined with Constraint Consensus method for solving COPs
NM Hamza, RA Sarker, DL Essam
The 2011 International Conference on Computer Engineering & Systems, 116-120, 2011
2011
Mathematical Modeling and Theory
D Patterson, D Della-Bosca, JL Pfaltz, AMFM AbdAllah, DL Essam, ...
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