Source Code

Evolutionary Computation (A Guide to Using Evolutionary Algorithms)

1. Source code of Convex Landscape Generator in W. Liu, S.Y. Yuen, K.W. Chung, and C.W. Sung, “A General-purpose Multi-dimensional convex landscape generator,” Mathematics, vol. 10(21), 3974, 2022.

2. Source code of

Y. Lou, S.Y. Yuen, and G. Chen, “Evolving benchmark functions using Kruskal-Wallis test,” Proc. Genetic and Evolutionary Computation Conference (GECCO), July 2018, pp. 1337-1341.

Y. Lou and S.Y. Yuen, "On constructing alternative benchmark suite for evolutionary algorithms," Swarm and Evolutionary Computation, vol. 44, pp. 287-292, 2019.

3. Source code of Continuous Non-revisiting Genetic Algorithm with Constant Memory (cNrGA/CM) in Y. Lou and S.Y. Yuen, “Non-revisiting genetic algorithm with adaptive mutation using constant memory,” Memetic Computing, vol. 8, no. 3, pp. 189-210, 2016.

4. Source code of One-Position Inheritance Artificial Bee Colony Algorithm (OPIABC) in X. Zhang and S.Y. Yuen, “Improving artificial bee colony with one-position inheritance mechanism,” Memetic Computing, vol. 5, no. 3, pp. 187-211, 2013.

5. Source code of Multiobjective Density-driven Evolutionary Algorithm (MODdEA) in C.K. Chow and S.Y. Yuen, “A multi-objective evolutionary algorithm that diversifies population by its density,” IEEE Transactions on Evolutionary Computation, vol. 16, no. 2, pp. 149-172, 2012.

6. Source code of History driven Evolutionary Algorithm (HdEA) in C.K. Chow and S.Y. Yuen, “An evolutionary algorithm that makes decision based on the entire previous search history,” IEEE Transactions on Evolutionary Computation, vol. 15, no. 6, pp. 741-769, 2011.

7. Source code of Non-revisiting Genetic Algorithm (NrGA) in S.Y. Yuen and C.K. Chow, “A Genetic algorithm that adaptively mutates and never revisits,” IEEE Transactions on Evolutionary Computation, vol. 13, no. 2, pp. 454-472, Apr. 2009.

The above works on search space with discrete variables (combinatorial problems). For search space with continuous variables (problems with bounded real variables), Continuous Non-revisiting Genetic Algorithm (cNrGA) may be used: Source code of Continuous Non-revisiting Genetic Algorithm (cNrGA) in C.K. Chow and S.Y. Yuen, “Continuous non-revisiting genetic algorithm with overlapped search sub-region,” Proc. IEEE Congress on Evolutionary Computation (CEC), June, 2012.

8. GA Simulator Source code of S.Y. Yuen and B.K.S. Cheung, “Bounds for probability of success of classical genetic algorithm based on Hamming distance,” IEEE Transactions on Evolutionary Computation, vol. 10, no. 1, pp. 1-18, Feb. 2006.

Computer Vision

1. Source code of S.Y. Yuen, Y.Y. Tsui and C.K. Chow, “A Fast marching formulation of perspective shape from shading under frontal illumination,” Pattern Recognition Letters, vol. 28, pp. 806-824, May 2007. Note: Please also refer to C.K. Chow and S.Y. Yuen, “Recovering shape by shading and stereo under Lambertian shading model,” International Journal of Computer Vision, vol. 85, no. 1, pp. 58-100, Oct. 2009 for an update and further experimental results on this method.

Machine Learning

1. NrSample (Atom 3.9) Source Code of J. Ahlgren and S.Y. Yuen, “Efficient program synthesis using constraint satisfaction in inductive logic programming,” Journal of Machine Learning Research, vol. 14, pp. 3649-3681, 2013. It can also be found in mirror site http://www.ahlgren.info/research/atom