Multi expression programming

Multi expression programming (MEP) is a technique for generating computer programs through programmed generation. It is one of several types of genetic programming, including linear genetic programming (LGP), traceless genetic programming (TGP), gene expression programming (GEP), Cartesian genetic programming (CGP), grammatical evolution (GE), genetic algorithm for deriving software (GADS), and fuzzy genetic programming (FGP). MEP is particularly useful for generating numerical expressions for regression and classification data analyses.From: Nature-Inspired Optimization Algorithms [2020], Prediction of stiffness modulus of bituminous mixtures using the applications of multi-expression programming and gene expression programming [2023]

Genetic Programming

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Published in A Vasuki, Nature-Inspired Optimization Algorithms, 2020

A Vasuki

There are different types of GP [6] such as linear genetic programming (LGP), traceless genetic programming (TGP), gene expression programming (GEP), multi-expression programming (MEP), Cartesian genetic programming (CGP), grammatical evolution (GE), genetic algorithm for deriving software (GADS), and fuzzy genetic programming (FGP).

Prediction of stiffness modulus of bituminous mixtures using the applications of multi-expression programming and gene expression programming

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Published in Road Materials and Pavement Design, 2023

Lee P. Leon, Hazi Azamathulla, Portia Felix, C. Venkata Siva Rama Prasad

The Multi-Expression Programming (MEP) is a technique for the programmed generation of computer programs, especially, being very well utilised for generating numerical articulations of regression and classification data analyses. MEP separates from other GP procedures by encoding numerous arrangements and solutions in a similar chromosome. This implies that the hunting space is better investigated. Such chromosomes are assessed (in the majority of the cases) in a similar intricacy on account of a solitary solution for every chromosome. The strategies additionally assess the computer programs’ guidance and store the incomplete outcomes for all training information. In this manner, MEP has an enormous speed without expecting to work at a low level, as the linear GP does. The chosen parameters settings utilised during MEP development are introduced in Table 5. The setting presented was used for developing all the MEP models in the study because it gave the best results out of the many sets of combinations evaluated.

Multi expression programming

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Genetic Programming

Prediction of stiffness modulus of bituminous mixtures using the applications of multi-expression programming and gene expression programming