Software & Datasets Associated with Published Papers

The codes were not developed professionally. The purpose of making the codes available is to allow other researchers to reproduce our reported results. For selected publications, we make available the codes for academic pursuits via "Publications" page. If you make use of these codes, please acknowledge the use of the codes, include the relevant papers in your list of references, and indicate the homepage address (http://www.ntu.edu.sg/home/epnsugan/) in your acknowledgement for the benefit of other researchers.

 If you wish to receive the codes of very recent publications, please send an email to epnsugan@ntu.edu.sg with your gmail / hotmail / yahoo email address and please indicate "software request" as the subject. You may also remind me if you do not hear from me within about 2 weeks.

 

Benchmark Problems for the Evaluation of Evolutionary algorithms

CEC-05 Invited Session on single objective optimization

Composition Functions

CEC-06 Invited Session/Competition on constrained single objective optimization

CEC-07 Invited Session/Competition on multi-objective optimization

CEC-08 Invited Session/Competition on large scale optimization

CEC-09 Invited Session/competition on multi-objective optimization

CEC-09 Invited Session/competition on dynamic optimization

CEC-10 Invited Session/Competition on constrained single objective optimization

CEC-10 Invited Session/Competition on large scale optimization

Competitions beyond 2010 ...

 

 

We also make available bio / bioinformatics datasets suitable to be used as benchmark datasets to test general pattern classification algorithms.

 Ensemble of Optimization Algorithms (EOAs),  Ensemble of Evolutionary Algorithms (EEAs) and  Ensemble Strategies for Evolutionary Algorithms

  1. R. Mallipeddi, S. Mallipeddi, P. N. Suganthan, “Ensemble strategies with adaptive evolutionary programming”, Information Sciences, accepted, DOI:10.1016/j.ins.2010.01.007.

  2. S. Z. Zhao and P. N. Suganthan, “Multi-objective Evolutionary Algorithm with Ensemble of External Archives”, Int. J. of Innovative Computing, Information and Control, May 2010.

  3. R. Mallipeddi, P. N. Suganthan, “Differential Evolution Algorithm with Ensemble of populations for Global Numerical Optimization”, OPSEARCH, vol. 46, no. 2, pp. 184-213, June 2009, Springer.

  4. E. L. Yu, P. N. Suganthan, "Ensemble of niching algorithms", Undergoing Review, 2009.

 

Evolutionary Programming

  1. R. Mallipeddi, P. N. Suganthan, “Empirical Study on the Performance of Single Objective Constraint Handling Techniques in Adaptive Evolutionary Programming”, IEEE Congress on Evolutionary Computation, pp. 4045-4052, Hong Kong, June 2008.

Particle Swarm Optimization

  1.  J. J. Liang, A. K. Qin, P. N. Suganthan and S. Baskar, “Evaluation of Comprehensive Learning Particle Swarm optimizer” Springer’s Lecture Notes in Computer Science, ICONIP’04, Vol. 3316, pp. 230-235, 2004.

  2. J. J. Liang, A. K. Qin, P. N. Suganthan and S. Baskar, "Comprehensive Learning Particle Swarm Optimizer for Global Optimization of Multimodal Functions", IEEE Transactions on Evolutionary Computations, Vol. 10, No. 3, pp. 281-295, June 2006. (Available in the package are FDRPSO, CLPSO, CPSO, UPSO, FIPSO, & various local/global PSO versions).

  3. V. L. Huang, P. N. Suganthan & J. J. Liang, “Comprehensive learning particle Swarm optimizer for solving multiobjective optimization problems”, Int. J of Int. Systems, Vol. 21, No. 2, pp. 209-226, 2006.

  4.  J. J. Liang and P. N. Suganthan, “Dynamic Multiswarm Particle Swarm Optimizer (DMS-PSO)", IEEE Swarm Intelligence Symp., pp. 124-129, June 2005.

  5. S. Z. Zhao, J. J. Liang, P. N. Suganthan, M. F. Tasgetiren, “Dynamic Multi-swarm Particle Swarm Optimizer with Local Search for Large Scale Global Optimization”, IEEE Congress on Evolutionary Computation, pp. 3845-3852, Hong Kong, June 2008.

  6. S. Z. Zhao and P. N. Suganthan, “Two-lbests Based Multi-objective Particle Swarm Optimizer”, Engineering Optimization, Accepted

  7. J. J. Liang and P. N. Suganthan, “Dynamic Multi-Swarm Particle Swarm Optimizer with a Novel Constraint-Handling Mechanism”, IEEE Congress on Evolutionary Computation, pp. 9-16, July 2006, Canada.

     

Differential Evolution

  1.  V. L. Huang, P. N. Suganthan and S. Baskar, "Multiobjective Differential Evolution with External Archive", Technical Report, Nanyang Technological University, Singapore, Dec. 2005.

  2. A. K. Qin and P. N. Suganthan, “Self-adaptive Differential Evolution Algorithm for Numerical Optimization”, IEEE Congress on Evolutionary Computation, pp. 1785-1791, Scotland, UK, Sept. 2005.

  3. V. L. Huang, A. K. Qin and P. N. Suganthan, “Self-adaptive Differential Evolution Algorithm for Constrained Real-Parameter Optimization”, IEEE Congress on Evolutionary Computation, pp. 7-24, July 2006, Canada.

  4. V. L. Huang, A, K. Qin, P. N. Suganthan and M. F. Tasgetiren, “Multi-objective Optimization based on Self-adaptive Differential Evolution Algorithm”, IEEE Congress on Evolutionary Computation, pp. 3601-3608, Sept. 2007, Singapore.

  5. A. K. Qin, V. L. Huang, P. N. Suganthan, Differential evolution algorithm with strategy adaptation for global numerical optimization", IEEE Transactions on Evolutionary Computations, Vol. 13, No. 2, pp. 398-417, April 2009. DOI: 10.1109/TEVC.2008.927706. (SaDE - Self-adaptive differential Evolution).

 

Bioinformatics Datasets / Software

(Please go to  http://web.mysites.ntu.edu.sg/epnsugan/PublicSite/Shared Documents/Forms/AllItems.aspx)

  1. E. K. Tang, P. N. Suganthan and X. Yao, “Gene selection algorithms for microarray data using least squares support vector machine”, BMC Bioinformatics, Feb. 2006, 7:95 (In folder "BMC-Feb-06").

  2. G. Pugalenthi G, K. Tang, P. N. Suganthan, G. Archunan, and R. Sowdhamini,  “Machine learning approach for the identification of odorant binding proteins from sequence-derived properties”, BMC Bioinformatics, Sep 19, Vol. 8, article  351, 2007 (In tar file "OBP-BMC-dataset-Sept-07.tar").

  3. G. Pugalenthi, K. Krishna Kumar, P. N. Suganthan and R. Gangal, “Identification of catalytic residues from protein structure using support vector machine with sequence and structural features”,  Biochemical and Biophysical Research Communications, Vol 367/3 pp. 630-634, Feb 2008 (In tar file "BBRC-08-dataset.tar").

  4. A. Anand, G. Pugalenthi, P. N. Suganthan, “Predicting protein structural class by SVM with class-wise optimized features and decision probabilities”, J. of Theoretical biology, 2008 (In folder "JTB-APS-2008").

  5. G. Pugalenthi, K. Tang, P. N. Suganthan and S. Chakrabarti, "Identification of structurally conserved residues of proteins in absence of structural homologs using neural network ensemble", Bioinformatics,  2009 Jan 15;25(2):204-10. Epub 2008 Nov 27 Click here to Download SCR-NN package.

  6. K. Krishna Kumar, G. Pugalenthi , P. N. Suganthan, “DNA-Prot: Identification of DNA binding proteins from protein sequence information using random forest”,  J of Biomolecular Structure & Dynamics, Vol. 26, No. 6, pp. 679-686, June 2009. Click here to Download the dataset

1.      

   Harmony Search

  1. Q-K Pan, P. N. Suganthan, M. F. Tasgetiren, J. J. Liang, "A Self-Adaptive Global Best Harmony Search Algorithm for Continuous Optimization Problems", Applied Mathematics and Computation, 216,  pp. 830-848, 2010. DOI: 10.1016/j.amc.2010.01.088.

  2. Q-K Pan, P. N. Suganthan, J. J. Liang , M. F. Tasgetiren, "A Local-Best Harmony Search Algorithm with Dynamic Subpopulations", Engineering Optimization, Vol. 42, Issue 2, February 2010, pp. 101 - 117. DOI: 10.1080/03052150903104366.

 

Genetic Algorithms

  1. J. J. Liang, S. Baskar, P. N. Suganthan and A. K. Qin, "Performance Evaluation of Multiagent Genetic Algorithm", Natural Computing, Vol. 5, No 1, pp. 83 – 96, Mar. 2006.

 

Multi-objective Evolutionary Algorithms

  1. V. L. Huang, P. N. Suganthan & J. J. Liang, “Comprehensive learning particle Swarm optimizer for solving multiobjective optimization problems”,  Int. J of Int. Systems, Vol. 21, No. 2, pp. 209-226, 2006.
  2. V. L. Huang, P. N. Suganthan and S. Baskar, "Multiobjective Differential Evolution with External Archive", Technical Report, Nanyang Technological University, Singapore, 2005.

  3. S. Z. Zhao and P. N. Suganthan, “Two-lbests Based Multi-objective Particle Swarm Optimizer”, Engineering Optimization, Accepted

 

Clustering 

  1. A. K.  Qin and P. N. Suganthan, “Enhanced neural gas network for prototype based clustering”, Pattern Recognition, 38(8):1275-1288, August 2005.

  2. A. K.  Qin and P. N. Suganthan, “Initialization insensitive LVQ algorithm based on cost function adaptation”, 38(5):773-776, Pattern Recognition, May 2005.

  3. A. K. Qin and P. N. Suganthan, “Robust growing neural gas algorithm with application in cluster analysis”, Neural Networks, Vol. 17, No. 8-9, pp. 1135-1148, Oct.-Nov. 2004.

 

Feature Extraction

1.      E. K. Tang, P. N. Suganthan, X. Yao and A. K. Qin, "Linear Dimensionality Reduction Using Relevance Weighted LDA", Vol. 38, No. 4, pp. 485-493, Pattern Recognition, April 2005.

 

Antenna Design Using Evolutionary Algorithms

  1. S. Baskar, A. Alphones and P. N. Suganthan, “Genetic Algorithm Based Design of Reconfigurable Antenna Array with Discrete Phase Shifters,” Microwave and Optical Technology Letters, 45(6):461-465, June 2005.

  2. S. Baskar, A. Alphones, P. N. Suganthan and J. J. Liang, “Design of Yagi-Uda Antennas Using Particle Swarm Optimization with new learning strategy”, IEE Proc. on Antenna and Propagation, 152(5):340-346 OCT. 2005.

 

Fiber Bragg Grating Filter & Sensor Network Design Using Evolutionary Algorithms

  1. S. Baskar, R. T. Zheng, A. Alphones, N. Q. Ngo and P. N.  Suganthan, “Particle Swarm Optimization for the Design of Low-Dispersion Fiber Bragg Gratings,” IEEE Photonics Technology Letters, 17(3):615-617, March 2005.

  2. S. Baskar, A. Alphones, P. N.  Suganthan, N. Q. Ngo and R. T. Zheng, "Design of Optimal Length Low Dispersion FBG filter Using Covariance Matrix Adapted Evolution", IEEE Photonics Technology Letters, 17(10):2119-2121 OCT 2005.

  3. S. Baskar, et al., "Design of triangular FBG filter for sensor applications using covariance matrix adapted evolution algorithm," Optics Communications, Vol. 260, No. 2, pp. 716-722, 15 April 2006.

  4. J. J. Liang, P. N. Suganthan, C. C. Chan and V. L. Huang, “Wavelength detection in FBG sensor network using tree search dynamic multi-swarm particle swarm optimizer”, IEEE Photonics Technology Letters, 18(12):1305 - 1307, Jun 15, 2006.