scholarly journals Correction: Combined mechanistic and genetic programming approach to modeling pilot NBR production: influence of feed compositions on rubber Mooney viscosity

RSC Advances ◽  
2021 ◽  
Vol 11 (11) ◽  
pp. 6395-6395
Author(s):  
Ge He ◽  
Tao Luo ◽  
Yagu Dang ◽  
Li Zhou ◽  
Yiyang Dai ◽  
...  
Keyword(s):  
Genetic Programming ◽  
Programming Approach ◽  
Mooney Viscosity

Correction for ‘Combined mechanistic and genetic programming approach to modeling pilot NBR production: influence of feed compositions on rubber Mooney viscosity’ by Ge He et al., RSC Adv., 2021, 11, 817–829, DOI: 10.1039/D0RA07257E.

scholarly journals Combined mechanistic and genetic programming approach to modeling pilot NBR production: influence of feed compositions on rubber Mooney viscosity

RSC Advances ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 817-829
Author(s):  
Ge He ◽  
Tao Luo ◽  
Yagu Dang ◽  
Li Zhou ◽  
Yiyang Dai ◽  
...  
Keyword(s):  
Genetic Programming ◽  
Emulsion Polymerization ◽  
Process Model ◽  
Mechanistic Model ◽  
Polymerization Kinetics ◽  
Data Driven ◽  
Programming Approach ◽  
Process Conditions ◽  
Model Based ◽  
Mooney Viscosity

The process model comprised of a mechanistic model based on emulsion polymerization kinetics and a data-driven model derived from genetic programming is developed to correlate the feed compositions and process conditions to NBR Mooney viscosity.

scholarly journals Binary Image Classification: A Genetic Programming Approach to the Problem of Limited Training Instances

2016 ◽  
Vol 24 (1) ◽  
pp. 143-182 ◽  
Author(s):  
Harith Al-Sahaf ◽  
Mengjie Zhang ◽  
Mark Johnston
Keyword(s):  
Computer Vision ◽  
Pattern Recognition ◽  
Genetic Programming ◽  
Visual System ◽  
Image Classification ◽  
Human Visual System ◽  
Binary Classification ◽  
Programming Approach ◽  
Data Sets ◽  
New Class

In the computer vision and pattern recognition fields, image classification represents an important yet difficult task. It is a challenge to build effective computer models to replicate the remarkable ability of the human visual system, which relies on only one or a few instances to learn a completely new class or an object of a class. Recently we proposed two genetic programming (GP) methods, one-shot GP and compound-GP, that aim to evolve a program for the task of binary classification in images. The two methods are designed to use only one or a few instances per class to evolve the model. In this study, we investigate these two methods in terms of performance, robustness, and complexity of the evolved programs. We use ten data sets that vary in difficulty to evaluate these two methods. We also compare them with two other GP and six non-GP methods. The results show that one-shot GP and compound-GP outperform or achieve results comparable to competitor methods. Moreover, the features extracted by these two methods improve the performance of other classifiers with handcrafted features and those extracted by a recently developed GP-based method in most cases.

scholarly journals A NEW LINEAR GENETIC PROGRAMMING APPROACH BASED ON STRAIGHT LINE PROGRAMS: SOME THEORETICAL AND EXPERIMENTAL ASPECTS

2009 ◽  
Vol 18 (05) ◽  
pp. 757-781 ◽  
Author(s):  
CÉSAR L. ALONSO ◽  
JOSÉ LUIS MONTAÑA ◽  
JORGE PUENTE ◽  
CRUZ ENRIQUE BORGES
Keyword(s):  
Data Structure ◽  
Genetic Programming ◽  
Computer Programs ◽  
Symbolic Regression ◽  
Programming Approach ◽  
Linear Genetic Programming ◽  
Straight Line ◽  
Structured Representations ◽  
Regression Problems ◽  
Straight Line Programs

Tree encodings of programs are well known for their representative power and are used very often in Genetic Programming. In this paper we experiment with a new data structure, named straight line program (slp), to represent computer programs. The main features of this structure are described, new recombination operators for GP related to slp's are introduced and a study of the Vapnik-Chervonenkis dimension of families of slp's is done. Experiments have been performed on symbolic regression problems. Results are encouraging and suggest that the GP approach based on slp's consistently outperforms conventional GP based on tree structured representations.

A Genetic Programming Approach for EUR/USD Exchange Rate Forecasting and Trading

2012 ◽  
Vol 42 (4) ◽  
pp. 415-431 ◽  
Author(s):  
Georgios A. Vasilakis ◽  
Konstantinos A. Theofilatos ◽  
Efstratios F. Georgopoulos ◽  
Andreas Karathanasopoulos ◽  
Spiros D. Likothanassis
Keyword(s):  
Exchange Rate ◽  
Genetic Programming ◽  
Programming Approach ◽  
Exchange Rate Forecasting
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