I hope I could find a tutorial introduction (hands on) in this topic "feature selection Using genetic algorithms"
I would suggest you review the section "3.3 Simple genetic search strategy" in Mark A. Hall's thesis "Correlation-based Feature Selection for Machine Learning" in the attached link.
Mainly, in feature selection, you have to select a subset of some given feature set. Therefore, feature selection is a good candidate for so many evolutionary and stochastic search methods because of its "subset finding" categorization. So I am sure you will find other modern research done in this field comparing different random search strategies.
http://www.cs.waikato.ac.nz/~mhall/thesis.pdf
please go through the attached links:
Punch III, W. F., Goodman, E. D., Pei, M., Chia-Shun, L., Hovland, P. D., & Enbody, R. J. (1993, June). Further Research on Feature Selection and Classification Using Genetic Algorithms. In ICGA (pp. 557-564).
Yang, J., & Honavar, V. (1998). Feature subset selection using a genetic algorithm. In Feature extraction, construction and selection (pp. 117-136). Springer US.
Jain, A., & Zongker, D. (1997). Feature selection: Evaluation, application, and small sample performance. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 19(2), 153-158.
http://garage.cse.msu.edu/papers/GARAGe93-1.pdf
http://www.sciencedirect.com/science/article/pii/0167865589900378
http://www.cs.msu.edu/~cse802/Papers/JainZongker_FeatureSelectionSmallSamplePerformance.pdf
Article Genetic Algorithms as a Strategy for Feature Selection
Here are several relevant referencesץ
Some of them contain good introduction and overview in this research area.
(1) Barlak, E. S. (2007). Feature selection using genetic algorithms.
(2) Kim, G., Kim, S., Tek, T., & Kyungki, S. (2000). Feature selection using genetic algorithms for handwritten character recognition.
(3) Hussein, F., Kharma, N., & Ward, R. (2001). Genetic algorithms for feature selection and weighting, a review and study. In Document Analysis and Recognition, 2001. Proceedings. Sixth International Conference on (pp. 1240-1244). IEEE.
(4) See chapter 4 in
Vafaie, H., & De Jong, K. (1993, November). Robust feature selection algorithms. In Tools with Artificial Intelligence, 1993. TAI'93. Proceedings., Fifth International Conference on (pp. 356-363). IEEE.
(5) Miller, M. T., Jerebko, A. K., Malley, J. D., & Summers, R. M. (2003, May). Feature selection for computer-aided polyp detection using genetic algorithms. In Medical Imaging 2003 (pp. 102-110). International Society for Optics and Photonics.
(6) Sikora, R., & Piramuthu, S. (2007). Framework for efficient feature selection in genetic algorithm based data mining. European Journal of Operational Research, 180(2), 723-737.
An open-source MATLAB implementation of Clustering using Evolutionary algorithms and Metaheuristics, is available to download here:
http://yarpiz.com/64/ypml101-evolutionary-clustering
In addition to basic clustering problem, considering fixed number of clusters, the problem of Automatic Clustering is also solved, in this implementation.
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