__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

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

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

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.

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.

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 GitHub)

(Please go to https://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

- 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.