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Binary Classification of Medical Images by Symbolic Regression
This experimental study investigates the application of symbolic regression to classification of medical diagnostic images, and the effectiveness of...
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Artificial Intelligence in Physical Sciences: Symbolic Regression Trends and Perspectives
Symbolic regression (SR) is a machine learning-based regression method based on genetic programming principles that integrates techniques and...
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Deep symbolic regression for numerical formulation of fundamental period in concentrically steel-braced RC frames
This research explores the use of Deep Symbolic Regression (DSR) to develop a sophisticated predictive model for the fundamental period of vibration...
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Crack Growth Rate Model Derived from Domain Knowledge-Guided Symbolic Regression
Machine learning (ML) has powerful nonlinear processing and multivariate learning capabilities, so it has been widely utilised in the fatigue field....
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Development and validation of a symbolic regression-based machine learning method to predict COVID-19 in-hospital mortality among vaccinated patients
PurposeThe continuous evolution of SARS-CoV-2 and possible future pandemics have risen concerns relevant to the effectiveness of the vaccines which...
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DNS-Based Turbulent Closures for Sediment Transport Using Symbolic Regression
This work aims to improve the turbulence modeling in RANS simulations for particle-laden flows. Using DNS data as reference, the errors of the model...
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Symbolic Regression Using Dynamic Structured Grammatical Evolution with Digit Concatenation and Linear Scaling
Symbolic Regression’s vast search space can lead to computational inefficiencies. However, Grammatical Evolution (GE) narrows down the search by...
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Machine Learning Control by Symbolic Regression for the Extended Optimal Control Problem of Robot Group
The extended optimal control problem is considered. In the problem, it is necessary to find such an optimal control function so that it not only...
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Retrieving the Refractive Index of a Biological Material via Symbolic Regression
Modeling the optical properties of biological structures allows the development of novel biocompatible materials. Using two different approaches, we...
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Generalisation in Genetic Programming for Symbolic Regression: Challenges and Future Directions
Symbolic regression, as a regression analysis technique, can find the structure and coefficients of a regression model simultaneously. Genetic...
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On Affine Symbolic Regression Trees for the Solution of Functional Problems
Symbolic regression has emerged from the more general method of Genetic Programming (GP) as a means of solving functional problems in physics and...
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Machine Learning Control for Mobile Robot by Approximation Extremals by Symbolic Regression
The control system synthesis problem is considered for mobile robot. It is necessary to find a control as function of state space vector that...
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DNS-Based Turbulent Closures for Sediment Transport Using Symbolic Regression
Particle-laden flows occur in many ways in natural and technological situations. Jain et al. [4] presented four DNS studies of sediment transport...
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Genetic programming with separability detection for symbolic regression
Genetic Programming (GP) is a popular and powerful evolutionary optimization algorithm that has a wide range of applications such as symbolic...
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A new imputation method based on genetic programming and weighted KNN for symbolic regression with incomplete data
Incompleteness is one of the problematic data quality challenges in real-world machine learning tasks. A large number of studies have been conducted...
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Symbolic Regression Using Genetic Programming with Chaotic Method-Based Probability Map**s
In this study, we propose a novel pre-learning approach for genetic programming (GP) that aims to investigate the effect of the probability of being...
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An efficient memetic genetic programming framework for symbolic regression
BackgroundSymbolic regression is one of the most common applications of genetic programming (GP), which is a popular evolutionary algorithm in...
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Kernel-based data transformation model for nonlinear classification of symbolic data
Symbolic data are usually composed of some categorical variables used to represent discrete entities in many real-world applications. Mining of...
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An Application of Fuzzy Symbolic Time-Series for Energy Demand Forecasting
In this paper, we present a new fuzzy symbolization technique for energy load forecasting with neural networks, FPLS-Sym. Symbolization techniques...
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Automatic Test Data Generation Symbolic and Concolic Executions
Dynamic-symbolic execution is a fantastic area of research from a software industry point of view.