Replacement Method and Enhanced Replacement Method Versus the Genetic Algorithm Approach for the Selection of Molecular Descriptors in QSPR/QSAR Theories

2010 ◽  
Vol 50 (9) ◽  
pp. 1542-1548 ◽  
Author(s):  
Andrew G. Mercader ◽  
Pablo R. Duchowicz ◽  
Francisco M. Fernández ◽  
Eduardo A. Castro
Keyword(s):  
Genetic Algorithm ◽  
Molecular Descriptors ◽  
Replacement Method ◽  
Genetic Algorithm Approach ◽  
Enhanced Replacement Method ◽  
Selection Of

Modified and enhanced replacement method for the selection of molecular descriptors in QSAR and QSPR theories

2008 ◽  
Vol 92 (2) ◽  
pp. 138-144 ◽  
Author(s):  
Andrew G. Mercader ◽  
Pablo R. Duchowicz ◽  
Francisco M. Fernández ◽  
Eduardo A. Castro
Keyword(s):  
Molecular Descriptors ◽  
Replacement Method ◽  
Enhanced Replacement Method ◽  
Selection Of

Genetic Operators for Catalog Design

1995 ◽  
Author(s):  
Susan E. Carlson ◽  
Todd A. Pegg
Keyword(s):  
Genetic Algorithm ◽  
Design Process ◽  
Genetic Information ◽  
Basic System ◽  
System Configuration ◽  
Genetic Operators ◽  
Catalog Design ◽  
Genetic Algorithm Approach ◽  
Subsequent Selection ◽  
Selection Of

Abstract Catalog design is the process of forming functional systems by assembling selected components from manufacturers’ catalogs. Generally, the engineer performs this design process in two steps: the selection of a basic system configuration, and the subsequent selection of specific components for that configuration. A genetic algorithm approach to catalog design will allow the integration of these two steps. This paper focuses on the four components of a genetic algorithm that must be developed for application to the catalog design problem. These operators include: a genetic representation of the design that includes its component parts and their connectivity, an initialization process for generating an initial set of designs, and a method for the exchange of the designs’ genetic information.

Effects of Case Removal in Prognostic Models

2001 ◽  
Vol 40 (01) ◽  
pp. 32-38 ◽  
Author(s):  
S. Vinterbo ◽  
L. Ohno-Machado
Keyword(s):  
Genetic Algorithm ◽  
Diagnostic Methods ◽  
Prognostic Models ◽  
Predictive Capability ◽  
En Bloc ◽  
Diagnostic Statistics ◽  
Genetic Algorithm Approach ◽  
Regression Diagnostic ◽  
Selection Of ◽  
Training Sets

AbstractConstructing and updating prognostic models that learn from training cases is a time-consuming task. The more compact, and yet informative, the training sets are, the faster one can build and properly evaluate such models. We have compared different regression diagnostic methods for selection and removal of training cases in prognostic models. Univariate determinations were performed using classical regression diagnostic statistics. Multivariate determinations were performed using (1) a sequential “backward” selection of cases, and (2) a non-sequential genetic algorithm. The genetic algorithm produced final models that kept few cases and retained predictive capability. A genetic algorithm approach to case selection may be better suited for guiding removal of cases in training sets than a univariate or a sequential multivariate approach, possibly because of its ability to detect sets of cases that are influential en bloc but may not be sufficiently influential when considered in isolation.

Feature selection of the armature winding broken coils in synchronous motor using genetic algorithm and mahalanobis distance

2012 ◽  
Vol 57 (3) ◽  
pp. 829-835 ◽  
Author(s):  
Z. Głowacz ◽  
J. Kozik
Keyword(s):  
Genetic Algorithm ◽  
Feature Selection ◽  
Mahalanobis Distance ◽  
Distance Measure ◽  
Synchronous Motor ◽  
Medical Diagnostics ◽  
Motor Current ◽  
Feature Spaces ◽  
Multidimensional Feature Spaces ◽  
Selection Of

The paper describes a procedure for automatic selection of symptoms accompanying the break in the synchronous motor armature winding coils. This procedure, called the feature selection, leads to choosing from a full set of features describing the problem, such a subset that would allow the best distinguishing between healthy and damaged states. As the features the spectra components amplitudes of the motor current signals were used. The full spectra of current signals are considered as the multidimensional feature spaces and their subspaces are tested. Particular subspaces are chosen with the aid of genetic algorithm and their goodness is tested using Mahalanobis distance measure. The algorithm searches for such a subspaces for which this distance is the greatest. The algorithm is very efficient and, as it was confirmed by research, leads to good results. The proposed technique is successfully applied in many other fields of science and technology, including medical diagnostics.

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