Noha Hamza
Noha 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
532014
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
432015
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
282016
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
162011
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
122014
Differential evolution with multi-constraint consensus methods for constrained optimization
NM Hamza, RA Sarker, DL Essam
Journal of Global Optimization 57 (2), 583-611, 2013
112013
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
Exploring the Feasible Space Using Constraint Consensus in Solving Constrained Optimization Problems
NM Hamza, DL Essam, RA Sarker
Australasian Conference on Artificial Life and Computational Intelligence …, 2016
22016
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
22014
Hybridizing constraint consensus methods with evolutionary algorithms for constrained optimization
N Hamza, R Sarker, D Essam
Engineering &Information Technology, UNSW Canberra, 2012
12012
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
2020
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
2020
Sensitivity-Based Change Detection for Dynamic Constrained Optimization
N Hamza, R Sarker, D Essam
IEEE Access 8, 103900-103912, 2020
2020
Search Techniques for Evolutionary Constrained Optimization.
NM Hamza
University of New South Wales, Canberra, Australia, 2016
2016
Mathematical Modeling and Theory
D Patterson, D Della-Bosca, JL Pfaltz, AMFM AbdAllah, DL Essam, ...
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, 0
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