scholarly journals Multi Expression Programming

2021 ◽  
Author(s):  
Mihai Oltean ◽  
D. Dumitrescu
Keyword(s):  
Gene Expression ◽  
Genetic Programming ◽  
Gene Expression Programming ◽  
Test Problems ◽  
Machine Code ◽  
Cartesian Genetic Programming ◽  
Mathematical Expressions ◽  
Multi Expression Programming ◽  
Complex Computer ◽  
Better Than

Abstract Multi Expression Programming (MEP) is a new evolutionary paradigm intended for solving computationally difficult problems. MEP individuals are linear entities that encode complex computer programs. MEP chromosomes are represented in the same way as C or Pascal compilers translate mathematical expressions into machine code. MEP is used for solving some difficult problems like symbolic regression and game strategy discovering. MEP is compared with Gene Expression Programming (GEP) and Cartesian Genetic Programming (CGP) by using several well-known test problems. For the considered problems MEP outperforms GEP and CGP. For these examples MEP is two magnitude orders better than CGP.

scholarly journals Multi Expression Programming

2021 ◽  
Author(s):  
Mihai Oltean ◽  
D. Dumitrescu
Keyword(s):  
Gene Expression ◽  
Genetic Programming ◽  
Gene Expression Programming ◽  
Test Problems ◽  
Machine Code ◽  
Cartesian Genetic Programming ◽  
Mathematical Expressions ◽  
Multi Expression Programming ◽  
Complex Computer ◽  
Better Than

Abstract Multi Expression Programming (MEP) is a new evolutionary paradigm intended for solving computationally difficult problems. MEP individuals are linear entities that encode complex computer programs. MEP chromosomes are represented in the same way as C or Pascal compilers translate mathematical expressions into machine code. MEP is used for solving some difficult problems like symbolic regression and game strategy discovering. MEP is compared with Gene Expression Programming (GEP) and Cartesian Genetic Programming (CGP) by using several well-known test problems. For the considered problems MEP outperforms GEP and CGP. For these examples MEP is two magnitude orders better than CGP.

GENETIC PROGRAMMING WITH LINEAR REPRESENTATION: A SURVEY

2009 ◽  
Vol 18 (02) ◽  
pp. 197-238 ◽  
Author(s):  
MIHAI OLTEAN ◽  
CRINA GROŞAN ◽  
LAURA DIOŞAN ◽  
CRISTINA MIHĂILĂ
Keyword(s):  
Gene Expression ◽  
Genetic Programming ◽  
Gene Expression Programming ◽  
Linear Representation ◽  
Computer Programs ◽  
Linear Genetic Programming ◽  
Cartesian Genetic Programming ◽  
Automated Method ◽  
Multi Expression Programming ◽  
High Level

Genetic Programming (GP) is an automated method for creating computer programs starting from a high-level description of the problem to be solved. Many variants of GP have been proposed in the recent years. In this paper we are reviewing the main GP variants with linear representation. Namely, Linear Genetic Programming, Gene Expression Programming, Multi Expression Programming, Grammatical Evolution, Cartesian Genetic Programming and Stack-Based Genetic Programming. A complete description is provided for each method. The set of applications where the methods have been applied and several Internet sites with more information about them are also given.

scholarly journals Predicting the Oil Well Production Based on Multi Expression Programming

2016 ◽  
Vol 9 (1) ◽  
pp. 21-32 ◽  
Author(s):  
Xin Ma ◽  
Zhi-bin Liu
Keyword(s):  
Gene Expression ◽  
Gene Expression Programming ◽  
Back Propagation ◽  
Petroleum Engineering ◽  
Reconstruction Technique ◽  
Oil Well ◽  
Empirical Methods ◽  
Well Production ◽  
Multi Expression Programming ◽  
Intelligent Methods

Predicting the oil well production is very important and also quite a complex mission for the petroleum engineering. Due to its complexity, the previous empirical methods could not perform well for different kind of wells, and intelligent methods are applied to solve this problem. In this paper the multi expression programming (MEP) method has been employed to build the prediction model for oil well production, combined with the phase space reconstruction technique. The MEP has shown a better performance than the back propagation networks, gene expression programming method and the Arps decline model in the experiments, and it has also been shown that the optimal state of the MEP could be easily obtained, which could overcome the over-fitting.

Gene Expression Programming

2017 ◽  
pp. 269-292 ◽  
Author(s):  
Baddrud Zaman Laskar ◽  
Swanirbhar Majumder
Keyword(s):  
Gene Expression ◽  
Genetic Algorithm ◽  
Natural Selection ◽  
Genetic Programming ◽  
Soft Computing ◽  
Gene Expression Programming ◽  
Research Work ◽  
Genetic Operators ◽  
A New Technique ◽  
Work Done

Gene expression programming (GEP) introduced by Candida Ferreira is a descendant of genetic algorithm (GA) and genetic programming (GP). It takes the advantage of both the optimization and search technique based on genetics and natural selection as GA and its programmatic Darwinian counterpart GP. It is gaining popularity because; it has to some extent eradicated the ‘cons' of both while keeping in the ‘pros'. It is still a new technique not much explored since its introduction in 2001. In this chapter both GA and GP is first discussed followed by the elaborate discussion of GEP. This is followed up by the discussion on research work done is different fields using GEP as a tool followed up by GEP architectures. Finally, here GEP has been used for detection of age from facial features as a soft computing based optimization problem using genetic operators.

scholarly journals Implementing Gene Expression Programming in the Parallel Environment for Big Datasets’ Classification

2019 ◽  
Vol 06 (02) ◽  
pp. 163-175 ◽  
Author(s):  
Joanna Jȩdrzejowicz ◽  
Piotr Jȩdrzejowicz ◽  
Izabela Wierzbowska
Keyword(s):  
Gene Expression ◽  
Parallel Processing ◽  
Genetic Programming ◽  
Open Source ◽  
Gene Expression Programming ◽  
Ensemble Classifier ◽  
Computational Experiments ◽  
Programming Library ◽  
Benchmark Datasets ◽  
Parallel Environment

The paper investigates a Gene Expression Programming (GEP)-based ensemble classifier constructed using the stacked generalization concept. The classifier has been implemented with a view to enable parallel processing with the use of Spark and SWIM — an open source genetic programming library. The classifier has been validated in computational experiments carried out on benchmark datasets. Also, it has been inbvestigated how the results are influenced by some settings. The paper is an extension of a previous paper of the authors.

Gene Expression Programming

2021 ◽  
pp. 427-446
Author(s):  
Baddrud Zaman Laskar ◽  
Swanirbhar Majumder
Keyword(s):  
Gene Expression ◽  
Genetic Algorithm ◽  
Natural Selection ◽  
Genetic Programming ◽  
Soft Computing ◽  
Gene Expression Programming ◽  
Research Work ◽  
Genetic Operators ◽  
A New Technique ◽  
Work Done

Gene expression programming (GEP) introduced by Candida Ferreira is a descendant of genetic algorithm (GA) and genetic programming (GP). It takes the advantage of both the optimization and search technique based on genetics and natural selection as GA and its programmatic Darwinian counterpart GP. It is gaining popularity because; it has to some extent eradicated the ‘cons' of both while keeping in the ‘pros'. It is still a new technique not much explored since its introduction in 2001. In this chapter both GA and GP is first discussed followed by the elaborate discussion of GEP. This is followed up by the discussion on research work done is different fields using GEP as a tool followed up by GEP architectures. Finally, here GEP has been used for detection of age from facial features as a soft computing based optimization problem using genetic operators.

Multi-Objective Optimization Algorithm Based on Gene Expression Programming and Virus Evolution

2011 ◽  
Vol 225-226 ◽  
pp. 372-376
Author(s):  
Wei Hong Wang ◽  
Yan Ye Du ◽  
Qu Li
Keyword(s):  
Gene Expression ◽  
Gene Expression Programming ◽  
Research Direction ◽  
Virus Evolution ◽  
Host Population ◽  
Test Problems ◽  
Local Optimum ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Multi Objective

Evolutionary Multi-objective Optimization (EMO) is a hot research direction nowadays and one of the state-of-the-art evolutionary multi-objective optimization algorithms ——NSGA-II has gain wide attention and application in many fields. Gene Expression Programming (GEP) has a powerful search capability, but falls into local optimum easily. Based on the transformed GEP, NSGA-II and the virus evolution mechanism, a new multi-objective evolutionary algorithm GEP Virus NSGA-II is proposed. With the infection operation of virus population, the diversity of the host population is increased, and it’s much easier to jump out of the local optimum. And this algorithm has got good experimental results on 9 standard test problems.

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