How to solve it: modern heuristics
How to solve it: modern heuristics
Further Research on Feature Selection and Classification Using Genetic Algorithms
Proceedings of the 5th International Conference on Genetic Algorithms
Parallel GA-Based Wrapper Feature Selection for Spectroscopic Data Mining
IPDPS '02 Proceedings of the 16th International Parallel and Distributed Processing Symposium
An introduction to variable and feature selection
The Journal of Machine Learning Research
Hybrid Genetic Algorithms for Feature Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Cooperative Coevolution: An Architecture for Evolving Coadapted Subcomponents
Evolutionary Computation
Improving real-parameter genetic algorithm with simulated annealing for engineering problems
Advances in Engineering Software
Parallelizing Feature Selection
Algorithmica
Genetic algorithms to solve the cover printing problem
Computers and Operations Research
Predictive models for the breeder genetic algorithm i. continuous parameter optimization
Evolutionary Computation
Expert Systems with Applications: An International Journal
A new genetic algorithm in proteomics: Feature selection for SELDI-TOF data
Computational Statistics & Data Analysis
Genetic algorithm-based feature selection in high-resolution NMR spectra
Expert Systems with Applications: An International Journal
Feature subspace ensembles: a parallel classifier combination scheme using feature selection
MCS'07 Proceedings of the 7th international conference on Multiple classifier systems
Evolutionary programming made faster
IEEE Transactions on Evolutionary Computation
An orthogonal genetic algorithm with quantization for globalnumerical optimization
IEEE Transactions on Evolutionary Computation
Hybrid Taguchi-genetic algorithm for global numerical optimization
IEEE Transactions on Evolutionary Computation
A robust stochastic genetic algorithm (StGA) for global numerical optimization
IEEE Transactions on Evolutionary Computation
Hybrid methods using genetic algorithms for global optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A multiagent genetic algorithm for global numerical optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Data mining and genetic algorithm based gene/SNP selection
Artificial Intelligence in Medicine
Two coding based adaptive parallel co-genetic algorithm with double agents structure
Engineering Applications of Artificial Intelligence
Expert Systems with Applications: An International Journal
Genetic regulatory network-based symbiotic evolution
Expert Systems with Applications: An International Journal
Constructing a novel mortality prediction model with Bayes theorem and genetic algorithm
Expert Systems with Applications: An International Journal
Computers in Biology and Medicine
Efficient ant colony optimization for image feature selection
Signal Processing
Hi-index | 12.06 |
Search algorithm is an essential part of feature selection algorithm. In this paper, through constructing double chain-like agent structure and with improved genetic operators, the authors propose one novel agent genetic algorithm-multi-population agent genetic algorithm (MPAGAFS) for feature selection. The double chain-like agent structure is more like local environment in real world, the introduction of this structure is good to keep the diversity of population. Moreover, the structure can help to construct multi-population agent GA, thereby realizing parallel searching for optimal feature subset. In order to evaluate the performance of MPAGAFS, several groups of experiments are conducted. The experimental results show that the MPAGAFS cannot only be used for serial feature selection but also for parallel feature selection with satisfying precision and number of features.