Results 81 to 90 of about 396,481 (140)

Optimal Power Flow Using Genetic Algorithm: Parametric Studies for Selection of Control and State Variables

, 2014
C. M. WANKHADE
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Regression Test Case Selection &PrioritizationUsing Dependence Graph and Genetic Algorithm

, 2014
Samaila Musa, Abu Bakar Md Sultan, Abdul Ghani, Salmi Baharom   +3 more
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ACCELERATED PSO SWARM SEARCH FEATURE SELECTION FOR DATA STREAM MINING BIG DATA USING GENETIC ALGORITHM

, 2015

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DWFS: A Wrapper Feature Selection Tool Based on a Parallel Genetic Algorithm

, 2015
Othman Soufan, Dimitrios Kleftogiannis, Panos Kalnis, Vladimir B. Bajić   +3 more
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The Optimization Decision Model of Sub-contractor Selection in Multiple Subproject and Its Solving Method of Genetic Algorithm

, 2015
Huiyang Xie
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REVIEW PAPER ON PROTEIN STRUCTURE PREDICTION USING VARIOUS SELECTION METHODS IN GENETIC ALGORITHM

, 2015

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Frequency selection using genetic algorithms

, 1993
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Quantum genetic selection [PDF]

Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2021
This paper proposes an innovative selection operator based on concepts from quantum mechanics. In particular, a quantum state is used to embody genetic individuals and their fitness values, and a quantum algorithm known as amplitude amplification is used to modify this state in order to create a quantum superposition in which the probability to measure
Acampora G., Schiattarella R., Vitiello A.   +2 more
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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.
Ting Kuo, Shu-Yuen Hwang
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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 ...
Yulu Qi, N. Chaikla
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