Genetic algorithms belong to the larger class of evolutionary algorithms (EA). Genetic algorithm generate solutions to optimization problems using techniques inspired by natural evolution, such as inheritance, mutation, selection, and crossover. In this paper various selection methods has been described ...
This paper introduces a modified version of a genetic algorithm with aggressive mutation (GAAM), one of the genetic algorithms (GAs) used for feature selec
The FS techniques can be broadly classified into filter-based and wrapper-based approaches in the supervised learning paradigm. The filter-based approaches select features using estimation criterion based on the statistics of learning data, and are independent of the induction classifier. The wrapper-...
The present study examines the role of feature selection methods in optimizing machine learning algorithms for predicting heart disease. The Cleveland Heart disease dataset with sixteen feature selection techniques in three categories of filter, wrapper,
Comparative study of different selection techniques in genetic algorithm. Int. J. Eng. Sci. Math. 2017, 6, 174–180. [Google Scholar] Takahashi, M.; Kita, H. A crossover operator using independent component analysis for real-coded genetic algorithms. In Proceedings of the 2001 Congress on ...
(Sun et al., 2019a). Optimization techniques, such asparticle swarm optimization(Chen et al., 2020), have also been used in feature selection.Sheikhan et al. (2013)used a combination ofANOVAand Tukey selection methods. Wrapping features can be done using open-source software platforms such ...
more,theunderlyingGeneticAlgorithmis unrestrictedlyincludedinallofthenewly proposedhybridvariantsunderespecialset- tings. 1 Introduction Optimizationtechniquesderivedfromnatureinclude SimulatedAnnealing(SA)whichdrawsananalogy betweentheannealingofmaterialtoitslowestener- geticstateandanoptimizationproblemorEvolution- aryA...
It is of interest to develop techniques for extracting useful information from the resulting data sets. Here we report the application of a two-way clustering method for analyzing a data set consisting of the expression patterns of different cell types. Gene expression in 40 tumor and 22 normal...
routinely used to generate useful solutions to optimization and search problems. Genetic algorithms belong to the larger class of evolutionary algorithms (EA), which generate solutions to optimization problems using techniques inspired by natural evolution, such as inheritance, mutation, selection...
while exploitation focuses on a localized search to refine solutions around local minima. Excessive exploration turns the algorithm into a random search, whereas excessive exploitation can result in premature convergence. This balance can be effectively achieved through hybridization techniques41, which combi...