Results 261 to 270 of about 1,270,531 (339)

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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A genetic algorithm for graphical model selection

Journal of the Italian Statistical Society, 1998
Graphical log-linear model search is usually performed by using stepwise procedures in which edges are sequentially added or eliminated from the independence graph. In this paper we implement the search procedure as a genetic algorithm and propose a crossover operator which operates on subgraphs. In a simulation study the proposed procedure is shown to
Poli I, Roverato A
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Genetic algorithms as a strategy for feature selection

Journal of Chemometrics, 1992
AbstractGenetic algorithms have been created as an optimization strategy to be used especially when complex response surfaces do not allow the use of better‐known methods (simplex, experimental design techniques, etc.). This paper shows that these algorithms, conveniently modified, can also be a valuable tool in solving the feature selection problem ...
LEARDI, RICCARDO, BOGGIA, RAFFAELLA, M. TERRILE   +2 more
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Selecting Simulation Algorithm Portfolios by Genetic Algorithms

2010 IEEE Workshop on Principles of Advanced and Distributed Simulation, 2010
An algorithm portfolio is a set of algorithms that are bundled together for increased overall performance. While being mostly applied to computationally hard problems so far, we investigate portfolio selection for simulation algorithms and focus on their application to adaptive simulation replication.
Rene Schulz, Adelinde M. Uhrmacher, Roland Ewald   +2 more
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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
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Android Malware Detection Using Genetic Algorithm based Optimized Feature Selection and Machine Learning

International Conference on Telecommunications and Signal Processing, 2019
Android platform due to open source characteristic and Google backing has the largest global market share. Being the world’s most popular operating system, it has drawn the attention of cyber criminals operating particularly through wide distribution of ...
Anam Fatima, Ritesh Maurya, M. Dutta, Radim Burget, J. Masek   +4 more
semanticscholar   +1 more source

Component Selection Using Genetic Algorithms [PDF]

19th Design Automation Conference: Volume 1 — Mechanical System Dynamics; Concurrent and Robust Design; Design for Assembly and Manufacture; Genetic Algorithms in Design and Structural Optimization, 1993
Abstract Genetic algorithms are investigated for use in obtaining optimal component configurations in dynamic engineering systems. Given a system layout, a database of component information from manufacturers’ catalogs, and a design specification, genetic algorithms are used to successfully select an optimal set of components.
Ronald W. Shonkwiler, M. E. Ingrim, Susan E. Carlson   +2 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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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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Genetic algorithms in feature and instance selection [PDF]

Knowledge-Based Systems, 2013
Feature selection and instance selection are two important data preprocessing steps in data mining, where the former is aimed at removing some irrelevant and/or redundant features from a given dataset and the latter at discarding the faulty data. Genetic algorithms have been widely used for these tasks in related studies.
Chih-Fong Tsai, Chi-Yuan Chu, William Eberle   +2 more
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