NOHA HAMZA received the MSc and PhD degrees in computer science from the University of New South Wales (UNSW) at Canberra, Australia, in 2012 and 2016, respectively. She is currently a Research Associate with the School of Engineering and Information Technology (SEIT), UNSW at Canberra. Her research interest includes the areas of evolutionary algorithms and constrained optimization.
Elsayed S; Sarker R; Hamza N; Coello CAC; Mezura-Montes E, 2020, 'Enhancing Evolutionary Algorithms by Efficient Population Initialization for Constrained Problems', in 2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Conference Proceedings, http://dx.doi.org/10.1109/CEC48606.2020.9185509
Hamza N; Sarker R; Essam D, 2020, 'Evolutionary Search from the Interior of Feasible Space', in 2020 IEEE Symposium Series on Computational Intelligence, SSCI 2020, pp. 353 - 359, http://dx.doi.org/10.1109/SSCI47803.2020.9308193
Elsayed S; Hamza N; Sarker R, 2016, 'Testing united multi-operator evolutionary algorithms-II on single objective optimization problems', in 2016 IEEE Congress on Evolutionary Computation, CEC 2016, pp. 2966 - 2973, http://dx.doi.org/10.1109/CEC.2016.7744164
Hamza NM; Essam DL; Sarker RA, 2016, 'Exploring the feasible space using constraint consensus in solving constrained optimization problems', in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 89 - 100, http://dx.doi.org/10.1007/978-3-319-28270-1_8
Elsayed SM; Sarker RA; Essam DL; Hamza NM, 2014, 'Testing united multi-operator evolutionary algorithms on the CEC2014 real-parameter numerical optimization', in Proceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014, pp. 1650 - 1657, http://dx.doi.org/10.1109/CEC.2014.6900308
Hamza ; Essam ; Sarker , 2014, 'Differential evolution with a constraint consensus mutation for solving optimization problems', in Proceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014, pp. 991 - 997, presented at 2014 IEEE Congress on Evolutionary Computation (CEC), 06 July 2014 - 11 July 2014, http://dx.doi.org/10.1109/CEC.2014.6900474
Hamza NM; Sarker R; Essam D, 2012, 'Differential evolution with a mix of Constraint Consenus methods for solving a real-world Optimization Problem', in Evolutionary Computation (CEC), 2012 IEEE Congress on, IEEE Press, USA, pp. 1 - 7, presented at Evolutionary Computation (CEC), 2012 IEEE Congress on, Brisbane, 10 June 2012 - 15 June 2012, http://dx.doi.org/10.1109/CEC.2012.6252904
Hamza NM; Elsayed ; Essam D; Sarker R, 2011, 'Differential evolution combined with constraint consensus for constrained optimization', in 2011 IEEE Congress of Evolutionary Computation, IEEE, New Orleans, LA, pp. 865 - 872, presented at 2011 IEEE Congress of Evolutionary Computation, CEC 2011, New Orleans, LA, 05 June 2011 - 08 June 2011, http://dx.doi.org/10.1109/CEC.2011.5949709
Hamza N; Sarker R; Essam D, 2011, 'Memetic Differential Evolution Combined with Constraint Consensus Method for solving COPs', in Computer Engineering & Systems (ICCES), 2011 International Conference on, IEEE Press, USA, pp. 116 - 120, presented at 2011 International Conference on Computer Engineering and Systems, ICCES'2011, Cairo, Egypt, 30 November 2011 - 01 December 2011, http://dx.doi.org/10.1109/ICCES.2011.6141023
Hamza N; Sarker R; Essam D, 2020, 'Sensitivity-Based Change Detection for Dynamic Constrained Optimization', IEEE Access, vol. 8, pp. 103900 - 103912, http://dx.doi.org/10.1109/ACCESS.2020.2999161
Hamza NM; Essam DL; Sarker RA, 2016, 'Constraint Consensus Mutation-Based Differential Evolution for Constrained Optimization', IEEE Transactions on Evolutionary Computation, vol. 20, pp. 447 - 459, http://dx.doi.org/10.1109/TEVC.2015.2477402
Hamza NM; Sarker RA; Essam DL; Deb K; Elsayed SM, 2014, 'A constraint consensus memetic algorithm for solving constrained optimization problems', Engineering Optimization, vol. 46, pp. 1447 - 1464, http://dx.doi.org/10.1080/0305215X.2013.846336
Hamza NM; Sarker RA; Essam DL, 2013, 'Differential evolution with multi-constraint consensus methods for constrained optimization', Journal of Global Optimization, vol. 57, pp. 583 - 611, http://dx.doi.org/10.1007/s10898-012-9987-z
- Winner of the IEEE-CEC2016’s Competition “Real-Parameter Numerical Optimization".
- Winner of the IEEE-CEC2014’s Competition “Real-Parameter Numerical Optimization".
- Scholarship for the "Research to Impact" program, Canberra Innovation Network (2019).
Dynamic optimization, Constrained optimization, Evolutionary computation.