| Whitley, D., Beveridge, J. R., Guerra-Salcedo, C., and Graves, C. Messy genetic algorithms for subset feature selection. In Proceedings of the 7th International Conference on Genetic Algorithms (San Francisco, July 19-23 1997), T. Back, Ed., Morgan Kaufmann, pp. 568-575. |
....of the new project. Kirsopp and Shepperd have many project attributes, some of which are better predictors than other. Determining the best set of attributes to use as the basis for a prediction, is a feature subset selection problem, to which search based approaches present reasonable solutions [63]. Interestingly, Kirsopp and Shepperd [32] like Mancoridis and Mitchell [19, 39] and Harman et al. 24] found that hill climbing out performed more elaborate search techniques for this problem. These results, taken together, give rise to optimism that progress can be made in Search Based ....
Whitley, D., Beveridge, J. R., Guerra-Salcedo, C., and Graves, C. Messy genetic algorithms for subset feature selection. In Proceedings of the 7th International Conference on Genetic Algorithms (San Francisco, July 19-23 1997), T. Back, Ed., Morgan Kaufmann, pp. 568-575.
....of the new project. Kirsopp and Shepperd have many project attributes, some of which are better predictors than other. Determining the best set of attributes to use as the basis for a prediction, is a feature subset selection problem, to which search based approaches present reasonable solutions [63]. Interestingly, Kirsopp and Shepperd [32] like Mancoridis and Mitchell [19, 39] and Harman et al. 24] found that hill climbing out performed more elaborate search techniques for this problem. These results, taken together, give rise to optimism that progress can be made in Search Based ....
WHITLEY, D., BEVERIDGE, J. R., GUERRA-SALCEDO, C., AND GRAVES, C. Messy genetic algorithms for subset feature selection. In Proceedings of the 7th International Conference on Genetic Algorithms (San Francisco, July 19-23 1997), T. Bick, Ed., Morgan Kaufmann, pp. 568-575.
.... of objectuve function evaluation with respect to increasing number of features) has been seriously questioned in different occasions (Goldberg, Korb, Deb 1989; Kargupta 1995; Thierens Goldberg 1993) As a result interest in data analysis using scalable genetic approahces (Kargupta et al. 1998; Whitley et al. 1997) is growing. The following section addresses the issue of scalable genetic algorithms. Decomposing blackbox search optimization Scalability (variation of performance quality with respect to growing problem difficulty, desired accuracy, reliability, computational resources) of DDM algo78 f # # # ....
Whitley, D.; Beveridge, R.; Guerra, C.; and Graves, C. 1997. Messy genetic algorithms for subset feature selection. In Punch, B., ed., Proceedings of the Seventh International Conference on Genetic Algorithms, not available. San Mateo, CA: Morgan Kaufmann.
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Whitley, D., Beveridge, J. R., Guerra-Salcedo, C. & Graves, C. 1997, `Messy genetic algorithm for subset feature selection', in Proceedings of the 7th International Conference on Genetic Algorithms , July 19 - 23, East Lansing, MI.
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