The DOI matches its Crossref record; 39 of the 39 references it cites were found in a registry or an index.
Differential evolution and particle swarm optimisation in partitional clustering
Open accessAbstract
Many partitional clustering algorithms based on genetic algorithms (GA) have been proposed to tackle the problem of finding the optimal partition of a data set. Very few studies considered alternative stochastic search heuristics other than GAs or simulated annealing. Two promising algorithms for numerical optimisation, which are hardly known outside the search heuristics field, are particle swarm optimisation (PSO) and differential evolution (DE). The performance of GAs for a representative point evolution approach to clustering is compared with PSO and DE. The empirical results show that DE is clearly and consistently superior compared to GAs and PSO for hard clustering problems, both with respect to precision as well as robustness (reproducibility) of the results. Only for simple data sets, the GA and PSO can obtain the same quality of results. Apart from superior performance, DE is easy to implement and requires hardly any parameter tuning compared to substantial tuning for GAs and PSOs. Our study shows that DE rather than GAs should receive primary attention in partitional clustering algorithms.
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Kennedy, J., & Eberhart, R. (2002) · Proceedings of ICNN'95 - International Conference on Neural Networks · doi:10.1109/icnn.1995.488968 · Cited by 58,192
Maximum Likelihood from Incomplete Data Via the EM Algorithm
Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1977.tb01600.x · Cited by 50,444
Holland, J. H. (1992) · The MIT Press eBooks · doi:10.7551/mitpress/1090.001.0001 · Cited by 47,002
Pattern Recognition with Fuzzy Objective Function Algorithms
Bezdek, J. C. (1981) · doi:10.1007/978-1-4757-0450-1 · Cited by 9,881
Everitt, B. J., Landau, S., & Leese, M. (1974) · Cited by 9,168
A dendrite method for cluster analysis
Caliński, T., & Harabasz, J. (1974) · Communications in Statistics · doi:10.1080/03610927408827101 · Cited by 4,480
Artificial Intelligence through Simulated Evolution
Fogel, L. J., Owens, A. J., & Walsh, M. J. (1966) · International Linear Collider · Cited by 2,796
Evolutionsstrategie : Optimierung technischer Systeme nach Prinzipien der biologischen Evolution
Rechenberg, I. (1973) · Frommann-Holzboog eBooks · Cited by 2,440
Lectu re Notes in Economics and Mathematical Systems
Beckmann, M. J., & Künzi, H. P. (1975) · Cited by 2,414
Model-Based Gaussian and Non-Gaussian Clustering
Banfield, J. D., & Raftery, A. E. (1993) · Biometrics · doi:10.2307/2532201 · Cited by 2,395
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Differential Evolution: A Survey of the State-of-the-Art
Das, S., & Suganthan, P. N. (2011) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2010.2059031 · Cited by 5,307
Das, S., Abraham, A., & Konar, A. (2008) · Studies in Computational Intelligence · doi:10.1007/978-3-540-78297-1_1 · Cited by 429
Particle swarm and ant colony algorithms hybridized for improved continuous optimization
Shelokar, P. S., Siarry, P., Jayaraman, V. K., & Kulkarni, B. D. (2007) · Applied Mathematics and Computation · doi:10.1016/j.amc.2006.09.098 · Cited by 344
Esmin, A. A. A., Coelho, R. A., & Matwin, S. (2015) · Artificial Intelligence Review · doi:10.1007/s10462-013-9400-4 · Cited by 340
Influence of crossover on the behavior of Differential Evolution Algorithms
Zaharie, D. (2009) · Applied Soft Computing · doi:10.1016/j.asoc.2009.02.012 · Cited by 302
Metadata
- Authors
- Paterlini, S., & Krink, T.
- Venue
- Computational Statistics & Data Analysis
- Publisher
- Elsevier BV
- Published
- 2006-03
- Volume
- 50
- Issue
- 5
- Pages
- 1220-1247
- ISSN
- 0167-9473 (ISSN-L)
- OpenAlex
- W2026789128
- Cited by
- 296
- References
- 39
- FWCI
- 14.77
- Type
- journal article
- Language
- en
- Open access
- green
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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