Classifier Ensembles Integration with Self-configuring Genetic Programming Algorithm

2013 ◽  
pp. 60-69 ◽  
Author(s):  
Maria Semenkina ◽  
Eugene Semenkin
Keyword(s):  
Genetic Programming ◽  
Programming Algorithm ◽  
Classifier Ensembles

scholarly journals Genetic Programming Algorithm for Designing of Control Systems

2018 ◽  
Vol 47 (4) ◽  
Author(s):  
Krzysztof Cpalka ◽  
Krystian Łapa ◽  
Andrzej Przybył
Keyword(s):  
Genetic Programming ◽  
Control Systems ◽  
Programming Algorithm

scholarly journals Prediction of Concrete Faced Rock Fill Dams Settlements Using Genetic Programming Algorithm

2012 ◽  
Vol 03 (03) ◽  
pp. 601-609 ◽  
Author(s):  
Seyed Morteza Marandi ◽  
Seyed Mahmood VaeziNejad ◽  
Elyas Khavari
Keyword(s):  
Genetic Programming ◽  
Programming Algorithm ◽  
Rock Fill

Evaluating Lateral Interactions of Motorized Two-Wheelers using Multi-Gene Symbolic Genetic Programming

2020 ◽  
Vol 2674 (9) ◽  
pp. 1120-1135 ◽  
Author(s):  
Sanhita Das ◽  
Narayana Raju ◽  
Akhilesh Kumar Maurya ◽  
Shriniwas Arkatkar
Keyword(s):  
Genetic Programming ◽  
Cognitive Architecture ◽  
Linear Structure ◽  
Symbolic Regression ◽  
Programming Algorithm ◽  
Lateral Interactions ◽  
Symbolic Model ◽  
Multigene Genetic Programming ◽  
Non Linear ◽  
Lateral Clearance

Complex maneuvering patterns are typical of motorized two-wheelers (MTWs), and their widespread adoption in many countries has spurred a growing response from transport researchers to model their dynamic behavior realistically. Considering the increased vulnerability of MTW drivers in dense urban mixed traffic systems, proper evaluation and modeling of lateral interactions between the drivers/riders moving abreast need to be addressed. A proper investigation can essentially help in understanding the behavioral aspects of riders in accepting shorter lateral clearances, design of exclusive motorcycle lanes, improved reliability of microsimulation models, and safety evaluation of the riders in a cognitive architecture. The current study therefore attempts to develop a novel symbolic regression model using a multigene genetic programming algorithm to generate and evaluate lateral clearance models naturally from field data for MTW interactions at mid-block sections, data being collected using video recorders. A binary logit model is initially developed to investigate the factors associated with the riders’ decisions to accept critical lateral clearances. Considering highly non-linear variations in data, the symbolic regression models were further developed and a comparison with the existing linear regression based lateral clearance models indicated that the symbolic model could generalize the non-linear structure of the data realistically and performed significantly better than the existing models.

A GENETIC PROGRAMMING-BASED LEARNING ALGORITHMS FOR PRUNING COST-SENSITIVE CLASSIFIERS

2012 ◽  
Vol 11 (02) ◽  
pp. 1250011 ◽  
Author(s):  
ZAHRA NIKDEL ◽  
HAMID BEIGY
Keyword(s):  
Decision Tree ◽  
Genetic Programming ◽  
Learning Algorithm ◽  
Fitness Function ◽  
Learning Algorithms ◽  
Hybrid Learning ◽  
Experimental Results ◽  
Programming Algorithm ◽  
Hybrid Learning Algorithm ◽  
Misclassification Costs

In this paper, we introduce a new hybrid learning algorithm, called DTGP, to construct cost-sensitive classifiers. This algorithm uses a decision tree as its basic classifier and the constructed decision tree will be pruned by a genetic programming algorithm using a fitness function that is sensitive to misclassification costs. The proposed learning algorithm has been examined through six cost-sensitive problems. The experimental results show that the proposed learning algorithm outperforms in comparison to some other known learning algorithms like C4.5 or naïve Bayesian.

Smooth fitting with a method for determining the regularization parameter under the genetic programming algorithm

2001 ◽  
Vol 133 (3-4) ◽  
pp. 175-194 ◽  
Author(s):  
Yun Seog Yeun ◽  
Kyung Ho Lee ◽  
Sang Min Han ◽  
Young Soon Yang
Keyword(s):  
Genetic Programming ◽  
Regularization Parameter ◽  
Programming Algorithm

G3P-MI: A genetic programming algorithm for multiple instance learning

2010 ◽  
Vol 180 (23) ◽  
pp. 4496-4513 ◽  
Author(s):  
Amelia Zafra ◽  
Sebastián Ventura
Keyword(s):  
Genetic Programming ◽  
Multiple Instance Learning ◽  
Programming Algorithm
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