Jul 12, 2014 · Our approach, called Multiple Regression Genetic Programming (MRGP) decouples and linearly combines a program's subexpressions via multiple regression on the target variable. The regression yields an alternate output: the prediction of the resulting multiple regression model.

Genetic programming has found recent success as a tool for learning sets of features for regression and classification. Multidimensional genetic programming is a useful variant of genetic programming for this task because it represents candidate solutions as sets of programs.

Jul 12, 2014 · We propose a new means of executing a genetic program which improves its output quality. Our approach, called Multiple Regression Genetic Programming (MRGP) decouples and linearly...

Multiple Regression Genetic Programming. MRGP is a hybrid method that combines tree-based Genetic Programming with LASSO. MRGP differs from conventional GP primarily in eliminating direct comparison of the final program output against the target variable, y.

– Multiple regression genetic programming , Ignacio Arnaldo, Krzysztof Krawiec, Una-May O'Reilly, GECCO '14, pp 879--886. • Option to accelerate runs with C++ optimized execution – Requires gccand g++ compilers, configuring Linux kernel parameter governing the maximum size of shared memory segments • Option to accelerate runs with CUDA ...

Multiple Regression Genetic Programming (MRGP) decou-ples and linearly combines a program’s subexpressions via multiple regression on the target variable. The regression yields an alternate output: the prediction of the resulting multiple regression model. It is this output, over many fit-ness cases, that we assess for fitness, rather than ...

Genetic programming (GP) is particularly suitable for symbolic regression, as it adaptively finds optimal solutions without requiring predefined model structures or data distributions. In recent years, extensive research has explored GP for single-output symbolic regression.

Aug 1, 2024 · In this paper, multi-output regression problems are regarded as multi-task optimization problems where predicting one output variable is considered as one task. A new multi-task multi-population genetic programming method is proposed to solve the problem.

Our approach, called Multiple Regression Genetic Programming (MRGP) decouples and linearly combines a program's subexpressions via multiple regression on the target variable. The regression yields an alternate output: the prediction of the resulting multiple regression model.

In this article, we propose a general multiform GP (MFGP) framework to improve the performance of GP on complicated SR problems. As far as we know, this articel is the first attempt to integrate the multiform optimization paradigm with GP to accelerate the search performance.