Page 42 - Fister jr., Iztok, Andrej Brodnik, Matjaž Krnc and Iztok Fister (eds.). StuCoSReC. Proceedings of the 2019 6th Student Computer Science Research Conference. Koper: University of Primorska Press, 2019
P. 42
using a violation factor. arXiv preprint [17] Y. Shi and R. Eberhart. A modified particle swarm
arXiv:1610.00976, 2016. optimizer. In 1998 IEEE International Conference on
Evolutionary Computation Proceedings. IEEE World
[2] J. Demˇsar. Statistical comparisons of classifiers over Congress on Computational Intelligence (Cat.
multiple data sets. Journal of Machine learning No.98TH8360), pages 69–73, May 1998.
research, 7(Jan):1–30, 2006.
[18] K. Socha. Ant Colony Optimization for Continuous
[3] D. Dua and C. Graff. UCI machine learning and Mixed-Variable Domains. PhD dissertation,
repository, 2017. Universite Libre de Bruxelles, 2014.
[4] R. C. Eberhart, Y. Shi, and J. Kennedy. Swarm [19] H. R. Tizhoosh. Opposition-based learning: a new
intelligence. Elsevier, 2001. scheme for machine intelligence. In International
Conference on Computational Intelligence for
[5] M. Friedman. The use of ranks to avoid the Modelling, Control and Automation and International
assumption of normality implicit in the analysis of Conference on Intelligent Agents, Web Technologies
variance. Journal of the American Statistical and Internet Commerce (CIMCA-IAWTIC’06),
Association, 32(200):675–701, 1937. volume 1, pages 695–701. IEEE, 2005.
[6] X. Han, L. Quan, X. Xiong, M. Almeter, J. Xiang, and [20] H.-C. Tsai. Unified particle swarm delivers high
Y. Lan. A novel data clustering algorithm based on efficiency to particle swarm optimization. Applied Soft
modified gravitational search algorithm. Engineering Computing, 55:371 – 383, 2017.
Applications of Artificial Intelligence, 61:1 – 7, 2017.
[21] G. Vrbanˇciˇc, L. Brezoˇcnik, U. Mlakar, D. Fister, and
[7] A. Hatamlou. Black hole: A new heuristic I. Fister Jr. NiaPy: Python microframework for
optimization approach for data clustering. Information building nature-inspired algorithms. Journal of Open
Sciences, 222:175 – 184, 2013. Including Special Source Software, 3, 2018.
Section on New Trends in Ambient Intelligence and
Bio-inspired Systems. [22] F. Wilcoxon. Individual comparisons by ranking
methods. In Breakthroughs in statistics, pages
[8] A. Hatamlou, S. Abdullah, and M. Hatamlou. Data 196–202. Springer, 1992.
clustering using big bang–big crunch algorithm. In
International Conference on Innovative Computing
Technology, pages 383–388. Springer, 2011.
[9] A. K. Jain. Data clustering: 50 years beyond k-means.
”Pattern Recognition Letters”, 31(8):651 – 666, 2010.
Award winning papers from the 19th International
Conference on Pattern Recognition (ICPR).
[10] J. Kennedy and R. Eberhart. Particle swarm
optimization. In Proceedings of IEEE International
Conference on Neural Networks, volume 4, pages 1942
– 1948 vol.4, 12 1995.
[11] 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
Computation, 10(3):281–295, June 2006.
[12] Y. Liu, Z. Qin, Z. Shi, and J. Lu. Center particle
swarm optimization. Neurocomputing,
70(4-6):672–679, 2007.
[13] S. J. Nanda and G. Panda. A survey on nature
inspired metaheuristic algorithms for partitional
clustering. Swarm and Evolutionary Computation,
16:1 – 18, 2014.
[14] P. Nemenyi. Distribution-free multiple comparisons
(doctoral dissertation, princeton university, 1963).
Dissertation Abstracts International, 25(2):1233, 1963.
[15] F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel,
B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer,
R. Weiss, V. Dubourg, J. Vanderplas, A. Passos,
D. Cournapeau, M. Brucher, M. Perrot, and
E. Duchesnay. Scikit-learn: Machine learning in
Python. Journal of Machine Learning Research,
12:2825–2830, 2011.
[16] F. Shahzad, A. R. Baig, S. Masood, M. Kamran, and
N. Naveed. Opposition-based particle swarm
optimization with velocity clamping (ovcpso). In
Advances in Computational Intelligence, pages
339–348. Springer, 2009.
StuCoSReC Proceedings of the 2019 6th Student Computer Science Research Conference 42
Koper, Slovenia, 10 October
arXiv:1610.00976, 2016. optimizer. In 1998 IEEE International Conference on
Evolutionary Computation Proceedings. IEEE World
[2] J. Demˇsar. Statistical comparisons of classifiers over Congress on Computational Intelligence (Cat.
multiple data sets. Journal of Machine learning No.98TH8360), pages 69–73, May 1998.
research, 7(Jan):1–30, 2006.
[18] K. Socha. Ant Colony Optimization for Continuous
[3] D. Dua and C. Graff. UCI machine learning and Mixed-Variable Domains. PhD dissertation,
repository, 2017. Universite Libre de Bruxelles, 2014.
[4] R. C. Eberhart, Y. Shi, and J. Kennedy. Swarm [19] H. R. Tizhoosh. Opposition-based learning: a new
intelligence. Elsevier, 2001. scheme for machine intelligence. In International
Conference on Computational Intelligence for
[5] M. Friedman. The use of ranks to avoid the Modelling, Control and Automation and International
assumption of normality implicit in the analysis of Conference on Intelligent Agents, Web Technologies
variance. Journal of the American Statistical and Internet Commerce (CIMCA-IAWTIC’06),
Association, 32(200):675–701, 1937. volume 1, pages 695–701. IEEE, 2005.
[6] X. Han, L. Quan, X. Xiong, M. Almeter, J. Xiang, and [20] H.-C. Tsai. Unified particle swarm delivers high
Y. Lan. A novel data clustering algorithm based on efficiency to particle swarm optimization. Applied Soft
modified gravitational search algorithm. Engineering Computing, 55:371 – 383, 2017.
Applications of Artificial Intelligence, 61:1 – 7, 2017.
[21] G. Vrbanˇciˇc, L. Brezoˇcnik, U. Mlakar, D. Fister, and
[7] A. Hatamlou. Black hole: A new heuristic I. Fister Jr. NiaPy: Python microframework for
optimization approach for data clustering. Information building nature-inspired algorithms. Journal of Open
Sciences, 222:175 – 184, 2013. Including Special Source Software, 3, 2018.
Section on New Trends in Ambient Intelligence and
Bio-inspired Systems. [22] F. Wilcoxon. Individual comparisons by ranking
methods. In Breakthroughs in statistics, pages
[8] A. Hatamlou, S. Abdullah, and M. Hatamlou. Data 196–202. Springer, 1992.
clustering using big bang–big crunch algorithm. In
International Conference on Innovative Computing
Technology, pages 383–388. Springer, 2011.
[9] A. K. Jain. Data clustering: 50 years beyond k-means.
”Pattern Recognition Letters”, 31(8):651 – 666, 2010.
Award winning papers from the 19th International
Conference on Pattern Recognition (ICPR).
[10] J. Kennedy and R. Eberhart. Particle swarm
optimization. In Proceedings of IEEE International
Conference on Neural Networks, volume 4, pages 1942
– 1948 vol.4, 12 1995.
[11] 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
Computation, 10(3):281–295, June 2006.
[12] Y. Liu, Z. Qin, Z. Shi, and J. Lu. Center particle
swarm optimization. Neurocomputing,
70(4-6):672–679, 2007.
[13] S. J. Nanda and G. Panda. A survey on nature
inspired metaheuristic algorithms for partitional
clustering. Swarm and Evolutionary Computation,
16:1 – 18, 2014.
[14] P. Nemenyi. Distribution-free multiple comparisons
(doctoral dissertation, princeton university, 1963).
Dissertation Abstracts International, 25(2):1233, 1963.
[15] F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel,
B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer,
R. Weiss, V. Dubourg, J. Vanderplas, A. Passos,
D. Cournapeau, M. Brucher, M. Perrot, and
E. Duchesnay. Scikit-learn: Machine learning in
Python. Journal of Machine Learning Research,
12:2825–2830, 2011.
[16] F. Shahzad, A. R. Baig, S. Masood, M. Kamran, and
N. Naveed. Opposition-based particle swarm
optimization with velocity clamping (ovcpso). In
Advances in Computational Intelligence, pages
339–348. Springer, 2009.
StuCoSReC Proceedings of the 2019 6th Student Computer Science Research Conference 42
Koper, Slovenia, 10 October
