Results 301 to 310 of about 586,435 (341)

Genetic algorithms in feature selection

IEEE SMC'99 Conference Proceedings. 1999 IEEE International Conference on Systems, Man, and Cybernetics (Cat. No.99CH37028), 2003
We use a genetic algorithm (GA) for the feature selection problem. The method explores the space of possible subsets to obtain the set of features that maximizes the predictive accuracy and minimizes irrelevant attributes. We introduce a multiple correlation in a fitness function used by the GA to evaluate the fitness of each feature subset regarding ...
N. Chaikla, null Yulu Qi
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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
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Biologically Inspired Parent Selection in Genetic Algorithms

Annals of Operations Research, 2019
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Drezner, Zvi, Drezner, Taly Dawn
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Gene Selection Using Genetic Algorithms

2004
Microarrays are emerging technologies that allow biologists to better understand the interactions between disease and normal states, at genes level. However, the amount of data generated by these tools becomes problematic when data are supposed to be automatically analyzed (e.g., for diagnostic purposes).
Bruno Feres de Souza, André C. P. L. F. de Carvalho   +1 more
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Portfolio Selection Using Genetic Algorithm [PDF]

, 2012
The selection of optimal portfolios is the central problem of financial investment decisions. Mathematically speaking, portfolio selection refers to the formulation of an objective function that determines the weights of the portfolio invested in each asset as to maximize return and minimize risk. This paper applies the method of genetic algorithm (GA)
sefiane, slimane, Benbouziane, Mohamed
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Selective Hydrogen Oxidation Catalysts via Genetic Algorithms

Advanced Synthesis & Catalysis, 2008
AbstractSolid “oxygen reservoirs,” such as doped ceria, can be successfully applied in a novel process for the oxidative dehydrogenation of propane. The ceria lattice oxygen selectively burns hydrogen from the dehydrogenation mixture at 550 °C. This gives three key advantages: it shifts the dehydrogenation equilibrium to the desired product side ...
Beckers, J., Clerc, F., Blank, J.H., Rothenberg, G.   +3 more
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Algorithm Selection on Adaptive Operator Selection: A Case Study on Genetic Algorithms

2021
The present study applies Algorithm Selection (AS) to Adaptive Operator Selection (AOS) for further improving the performance of the AOS methods. AOS aims at delivering high performance in solving a given problem through combining the strengths of multiple operators.
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GrC model in Genetic Algorithm: Artificial Selection Algorithm

2008 IEEE International Conference on Granular Computing, 2008
Genetic Algorithm (GA), a programming technique that mimics natural evolution as a problem-solving strategy, has become popular since its appearance. It keeps the properties similar to natural selection systems. Many improved GAs has been proposed, however, natural selection essence is not changed.
Z.H. Chen, G.W. Yan, G. Xie, K. M. Xie
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Enhancing Data Selection Using Genetic Algorithm

2010 International Conference on Computational Intelligence and Communication Networks, 2010
Genetic algorithms are becoming increasingly valuable in solving large-scale, realistic, difficult problems, and selecting replica with multiple selection criteria - availability, security and time- is one of these problems. In this paper, a rank based elitist clustering Genetic Algorithm is proposed named RRWSGA, which alleviates the problem of being ...
O A Jadaan, W Abdulal, M A Hameed, A Jabas   +3 more
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Entropy-Boltzmann selection in the genetic algorithms

IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics), 2003
A new selection method, entropy-Boltzmann selection, for genetic algorithms (GAs) is proposed. This selection method is based on entropy and importance sampling methods in Monte Carlo simulation. It naturally leads to adaptive fitness in which the fitness function does not stay fixed but varies with the environment.
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