Research Resources

Evolutionary Optimization Toolbox (EOT) is designed for solving single objective static optimization problems (both constrained and unconstrained) and dynamic constrained optimization problems, all in continuous domain. It is initially designed for the Windows 64 bit operating system. Please follow the readme file before the first use.

Relevant papers

Elsayed, S., Sarker, R., Coello Coello, C.: Sequence-based Deterministic Initialization for Evolutionary Algorithms. IEEE Transactions on Cybernetics, 47(9), pp.2911-2923, 2017. (Code- Example) (preprint) (appendices)

Elsayed, S., Hamza, N., Sarker, R.: Testing United Multi-operator Evolutionary Algorithms-II on Single Objective Optimization Problems. In: IEEE Congress on Evolutionary Computation, Vancouver, 2016, pp. 2966-2973. Joint winner (code) (preprint)

S. Elsayed and R. Sarker. An Adaptive Configuration of Differential Evolution Algorithms for Big Data. IEEE Congress on Evolutionary Computation, Sendai, Japan, 2015, pp. 695-702, ranked 3rd, code and results for single and multi-objective problems, paper (pre-print), competition info.

S. Elsayed, R. Sarker and D. Essam (2011). GA with a New Multi-Parent Crossover for Solving IEEE-CEC2011 Competition Problems, IEEE Congress on Evolutionary Computation, New Orleans, USA, pp.1034-1040 download code (v.1)

N. Hamza, R. Sarker and D. Essam (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

Talks - Video

IEEE-WCCI CEC2020 Keynote Speech delivered by Ruhul Sarker - watch the video from this link

Google Sites

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