An improved branch and bound algorithm for feature selection
Pattern Recognition Letters
A genetic algorithm for dynamic advanced planning and scheduling (DAPS) with a frozen interval
Expert Systems with Applications: An International Journal
Classifier design with feature selection and feature extraction using layered genetic programming
Expert Systems with Applications: An International Journal
Genetic algorithm-based feature set partitioning for classification problems
Pattern Recognition
Genetic algorithm-based feature selection in high-resolution NMR spectra
Expert Systems with Applications: An International Journal
An optimum feature extraction method for texture classification
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
The search for optimal feature set in power quality event classification
Expert Systems with Applications: An International Journal
A new approach for image processing in foreign fiber detection
Computers and Electronics in Agriculture
An Automated Visual Inspection System for Foreign Fiber Detection in Lint
GCIS '09 Proceedings of the 2009 WRI Global Congress on Intelligent Systems - Volume 04
Optimal training subset in a support vector regression electric load forecasting model
Applied Soft Computing
Genetic algorithm-based heuristic for feature selection in credit risk assessment
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
This paper presents an improved genetic algorithm (IGA) by which the optimal feature subset can be selected effectively and efficiently from a multi-character feature set (MCFS). IGA adopts segmented chromosome management scheme to implement local management of chromosome. This scheme encodes a solution with an entire binary chromosome; but logically, it divides the chromosome into several segments according to the number of feature groups in MCFS for local management. A segmented crossover operator and a segmented mutation operator are employed to operate on these segments to avoid invalid chromosomes. The probability of crossover and mutation are adjusted dynamically according to the generation number and the fitness value. As a result, IGA obtains strong searching ability at the beginning of the evolution and achieves accelerated convergence along the evolution. IGA is tested using features extracted from cotton foreign fiber objects, and compared with the Simple Genetic Algorithm (SGA) under the same condition. The results show that IGA receives improved searching ability and convergence speed compared with SGA. The optimal feature subset selected by the IGA has much smaller size than that of the SGA. This is very important for the online classification of foreign fibers.