Results 251 to 260 of about 1,388,322 (334)

Feature subset selection using a genetic algorithm

IEEE Intelligent Systems, 1998
Practical pattern-classification and knowledge-discovery problems require the selection of a subset of attributes or features to represent the patterns to be classified. The authors' approach uses a genetic algorithm to select such subsets, achieving multicriteria optimization in terms of generalization accuracy and costs associated with the features.
Yang, Jihoon, Honavar, Vasant
openaire   +3 more sources

Some of the next articles are maybe not open access.

Related searches:

A k-NN method for lung cancer prognosis with the use of a genetic algorithm for feature selection

Expert systems with applications, 2021
Lung cancer is one of the most common diseases for human beings everywhere throughout the world. Early identification of this disease is the main conceivable approach to enhance the possibility of patients’ survival.
Negar Maleki, Yasser Zeinali, S. T. A. Niaki   +2 more
semanticscholar   +1 more source

A problem-specific non-dominated sorting genetic algorithm for supervised feature selection

Information Sciences, 2021
Feature selection (FS), which plays an important role in classification tasks, has been recently studied as a multi-objective optimization problem (MOP).
Yu Zhou, W. Zhang, Junhao Kang, Xiao Zhang, Xu Wang   +4 more
semanticscholar   +1 more source

A fast and elitist multiobjective genetic algorithm: NSGA-II

IEEE Transactions on Evolutionary Computation, 2002
K. Deb, S. Agrawal, Amrit Pratap, T. Meyarivan   +3 more
exaly   +2 more sources

A genetic algorithm with disruptive selection

IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics), 1996
Genetic algorithms are a class of adaptive search techniques based on the principles of population genetics. The metaphor underlying genetic algorithms is that of natural evolution. Applying the "survival-of-the-fittest" principle, traditional genetic algorithms allocate more trials to above-average schemata.
T, Kuo, S Y, Hwang
openaire   +2 more sources

Genetic Algorithm Guided Selection:  Variable Selection and Subset Selection

Journal of Chemical Information and Computer Sciences, 2002
A novel Genetic Algorithm guided Selection method, GAS, has been described. The method utilizes a simple encoding scheme which can represent both compounds and variables used to construct a QSAR/QSPR model. A genetic algorithm is then utilized to simultaneously optimize the encoded variables that include both descriptors and compound subsets.
Sung Jin, Cho, Mark A, Hermsmeier
openaire   +2 more sources