scholarly journals Selection of ψ-Caputo derivatives’ functional parameters in generalized water transport equation by genetic programming technique

Author(s):  
Vsevolod Bohaienko
2003 ◽  
Vol 11 (2) ◽  
pp. 169-206 ◽  
Author(s):  
Riccardo Poli ◽  
Nicholas Freitag McPhee

This paper is the second part of a two-part paper which introduces a general schema theory for genetic programming (GP) with subtree-swapping crossover (Part I (Poli and McPhee, 2003)). Like other recent GP schema theory results, the theory gives an exact formulation (rather than a lower bound) for the expected number of instances of a schema at the next generation. The theory is based on a Cartesian node reference system, introduced in Part I, and on the notion of a variable-arity hyperschema, introduced here, which generalises previous definitions of a schema. The theory includes two main theorems describing the propagation of GP schemata: a microscopic and a macroscopic schema theorem. The microscopic version is applicable to crossover operators which replace a subtree in one parent with a subtree from the other parent to produce the offspring. Therefore, this theorem is applicable to Koza's GP crossover with and without uniform selection of the crossover points, as well as one-point crossover, size-fair crossover, strongly-typed GP crossover, context-preserving crossover and many others. The macroscopic version is applicable to crossover operators in which the probability of selecting any two crossover points in the parents depends only on the parents' size and shape. In the paper we provide examples, we show how the theory can be specialised to specific crossover operators and we illustrate how it can be used to derive other general results. These include an exact definition of effective fitness and a size-evolution equation for GP with subtree-swapping crossover.


2009 ◽  
Vol 36 (3) ◽  
pp. 5157-5161 ◽  
Author(s):  
Koun-Tem Sun ◽  
Yi-Chun Lin ◽  
Cheng-Yen Wu ◽  
Yueh-Min Huang

Author(s):  
B. Samanta

Applications of genetic programming (GP) include many areas. However applications of GP in the area of machine condition monitoring and diagnostics is very recent and yet to be fully exploited. In this paper, a study is presented to show the performance of machine fault detection using GP. The time domain vibration signals of a rotating machine with normal and defective gears are processed for feature extraction. The extracted features from original and preprocessed signals are used as inputs to GP for two class (normal or fault) recognition. The number of features and the features are automatically selected in GP maximizing the classification success. The results of fault detection are compared with genetic algorithm (GA) based artificial neural network (ANN)- termed here as GA-ANN. The number of hidden nodes in the ANN and the selection of input features are optimized using GAs. Two different normalization schemes for the features have been used. For each trial, the GP and GA-ANN are trained with a subset of the experimental data for known machine conditions. The trained GP and GA-ANN are tested using the remaining set of data. The procedure is illustrated using the experimental vibration data of a gearbox. The results compare the effectiveness of both types of classifiers with GP and GA based selection of features.


Author(s):  
Kenneth Hennessy ◽  
Michael G. Madden ◽  
Jennifer Conroy ◽  
Alan G. Ryder

Soil Research ◽  
2012 ◽  
Vol 50 (6) ◽  
pp. 443 ◽  
Author(s):  
José Padarian ◽  
Budiman Minasny ◽  
Alex McBratney

The difference between the International (adopted by Australia) and the USDA/FAO particle-size classification systems is the limit between silt and sand fractions (20 μm for the International and 50 µm for the USDA/FAO). In order to work with pedotransfer functions generated under the USDA/FAO system with Australian soil survey data, a conversion should be attempted. The aim of this work is to improve prior models using larger datasets and a genetic programming technique, in the form of a symbolic regression. The 2–50 µm fraction was predicted using a USDA dataset which included both particle-size classification systems. The presented model reduced the root mean square error (%) by 14.96 and 23.62% (IGBP-DIS dataset and Australian dataset, respectively), compared with the previous model.


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