Can tree adjunct grammar guided genetic programming be good at finding a needle in a haystack? A case study

2003 ◽  
Author(s):  
N.X. Hoai ◽  
R.I. McKay ◽  
D. Essam
Keyword(s):  
Genetic Programming

scholarly journals Modelling streamflow response to hydro-climatic variables in the Upper Mkomazi River, South Africa

2014 ◽  
Author(s):  
◽  
Oluwaseun Kunle Oyebode
Keyword(s):  
South Africa ◽  
Water Resources ◽  
Genetic Programming ◽  
Predictive Models ◽  
Model Development ◽  
De Algorithm ◽  
Planning And Management ◽  
Ann Models ◽  
Artificial Neural

Streamflow modelling remains crucial to decision-making especially when it concerns planning and management of water resources systems in water-stressed regions. This study proposes a suitable method for streamflow modelling irrespective of the limited availability of historical datasets. Two data-driven modelling techniques were applied comparatively so as to achieve this aim. Genetic programming (GP), an evolutionary algorithm approach and a differential evolution (DE)-trained artificial neural network (ANN) were used for streamflow prediction in the upper Mkomazi River, South Africa. Historical records of streamflow and meteorological variables for a 19-year period (1994- 2012) were used for model development and also in the selection of predictor variables into the input vector space of the models. In both approaches, individual monthly predictive models were developed for each month of the year using a 1-year lead time. Two case studies were considered in development of the ANN models. Case study 1 involved the use of correlation analysis in selecting input variables as employed during GP model development, while the DE algorithm was used for training and optimizing the model parameters. However in case study 2, genetic programming was incorporated as a screening tool for determining the dimensionality of the ANN models, while the learning process was further fine-tuned by subjecting the DE algorithm to sensitivity analysis. Altogether, the performance of the three sets of predictive models were evaluated comparatively using three statistical measures namely, Mean Absolute Percent Error (MAPE), Root Mean-Squared Error (RMSE) and coefficient of determination (R2). Results showed better predictive performance by the GP models both during the training and validation phases when compared with the ANNs. Although the ANN models developed in case study 1 gave satisfactory results during the training phase, they were unable to extensively replicate those results during the validation phase. It was found that results from case study 1 were considerably influenced by the problems of overfitting and memorization, which are typical of ANNs when subjected to small amount of datasets. However, results from case study 2 showed great improvement across the three evaluation criteria, as the overfitting and memorization problems were significantly minimized, thus leading to improved accuracy in the predictions of the ANN models. It was concluded that the conjunctive use of the two evolutionary computation methods (GP and DE) can be used to improve the performance of artificial neural networks models, especially when availability of datasets is limited. In addition, the GP models can be deployed as predictive tools for the purpose of planning and management of water resources within the Mkomazi region and KwaZulu-Natal province as a whole.

Generating metamorphic relations for cyber-physical systems with genetic programming: an industrial case study

2021 ◽  
Author(s):  
Jon Ayerdi ◽  
Valerio Terragni ◽  
Aitor Arrieta ◽  
Paolo Tonella ◽  
Goiuria Sagardui ◽  
...  
Keyword(s):  
Genetic Programming ◽  
Cyber Physical Systems ◽  
Industrial Case Study ◽  
Physical Systems

scholarly journals GPML: an XML-based standard for the interchange of genetic programming trees

2019 ◽  
Vol 21 (4) ◽  
pp. 605-627 ◽  
Author(s):  
Tiantian Dou ◽  
Yuri Kaszubowski Lopes ◽  
Peter Rockett ◽  
Elizabeth A. Hathway ◽  
Esmail Saber
Keyword(s):  
Genetic Programming ◽  
Markup Language ◽  
Formal Definition ◽  
Model Predictive Controller ◽  
Predictive Controller ◽  
Definition Of ◽  
Heating Plant

AbstractWe propose a genetic programming markup language (GPML), an XML-based standard for the interchange of genetic programming trees, and outline the benefits such a format would bring in allowing the deployment of trained genetic programming (GP) models in applications as well as the subsidiary benefit of allowing GP researchers to directly share trained trees. We present a formal definition of this standard and describe details of an implementation. In addition, we present a case study where GPML is used to implement a model predictive controller for the control of a building heating plant.

scholarly journals Acceleration of Genetic Programming by Hierarchical Structure Learning: A Case Study on Image Recognition Program Synthesis

2009 ◽  
Vol E92-D (10) ◽  
pp. 2094-2102 ◽  
Author(s):  
Ukrit WATCHAREERUETAI ◽  
Tetsuya MATSUMOTO ◽  
Noboru OHNISHI ◽  
Hiroaki KUDO ◽  
Yoshinori TAKEUCHI
Keyword(s):  
Genetic Programming ◽  
Image Recognition ◽  
Hierarchical Structure ◽  
Structure Learning ◽  
Program Synthesis

scholarly journals A genetic programming approach to development of clinical prediction models: A case study in symptomatic cardiovascular disease

PLoS ONE ◽  
2018 ◽  
Vol 13 (9) ◽  
pp. e0202685 ◽  
Author(s):  
Christian A. Bannister ◽  
Julian P. Halcox ◽  
Craig J. Currie ◽  
Alun Preece ◽  
Irena Spasić
Keyword(s):  
Cardiovascular Disease ◽  
Genetic Programming ◽  
Prediction Models ◽  
Programming Approach ◽  
Clinical Prediction ◽  
Clinical Prediction Models

scholarly journals Hybridizing ANN-NSGA-II Model with Genetic Programming Method for Reservoir Operation Rule Curve Determination (Case Study Zayandehroud Dam Reservoir)

2021 ◽  
Author(s):  
ramtin moeini ◽  
kamran nasiri
Keyword(s):  
Genetic Programming ◽  
Hybrid Method ◽  
Stochastic Dynamic ◽  
Ann Model ◽  
Dam Reservoir ◽  
Nsga Ii ◽  
Rule Curves ◽  
Operation Rule ◽  
Input Variables

Abstract One of the most important and effective works of water resource planning and management is determining the specific, applicable, regulated operating policies of the Zayandehroud dam reservoir, as a case study, in which it should be user-friendly and straightforward for the operator. For this purpose, different methods have been proposed in which each of them has its limitations. Due to the unique capabilities of the genetic programming (GP) model, here, this method is used to determine the operating rule curves and policies of the dam reservoir. For this purpose, here, two cases are proposed in which, in the first case, each month is individually simulated and modeled. However, in the second case, all months are simulated simultaneously. A second case is proposed here to determine simple and more applicable operation rule curves. In addition, two approaches are suggested for each case in which in the first approach, the influential input variables are selected by presenting the hybrid method. In the proposed hybrid method, the artificial neural network (ANN) model is equipped with non dominated sorting genetic (NSGA-II) algorithm leading to a hybrid method named the ANN-NSGA-II method. However, in the second approach, the influential input variables are selected automatically using the GP method. Here, the hybrid method is proposed and used to overcome the limitations of existing usual method. In other words, it is proposed to reduce the number of influential input variables of data-driven methods and select the effective ones. The obtained results of all proposed cases and approaches are presented and compared with the standard operation policy (SOP) method, stochastic dynamic programming (SDP), ANN model and, NLP method. Comparison of the results shows the acceptable performance of the proposed cases and approaches. In other words, the best- obtained values of (stability index) SI index and water deficit (objective function value) are 49.3% and 32, respectively.

A Surrogate Genetic Programming Based Model to Facilitate Robust Multi-Objective Optimization: A Case Study in Magnetostatics

2013 ◽  
Vol 49 (5) ◽  
pp. 2065-2068 ◽  
Author(s):  
Marcus H. S. Mendes ◽  
Gustavo L. Soares ◽  
Jean-Louis Coulomb ◽  
Joao A. Vasconcelos
Keyword(s):  
Genetic Programming ◽  
Multi Objective Optimization ◽  
Multi Objective
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