System Identification Using Genetic Programming and Gene Expression Programming

2005 ◽  
pp. 503-511 ◽  
Author(s):  
Juan J. Flores ◽  
Mario Graff
Keyword(s):  
Gene Expression ◽  
System Identification ◽  
Genetic Programming ◽  
Gene Expression Programming

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.

Different Approaches in Genetic Programming

2018 ◽  
pp. 102-130
Keyword(s):  
Gene Expression ◽  
Genetic Programming ◽  
Gene Expression Programming ◽  
Computer Programs ◽  
Grammatical Evolution ◽  
Linear Genetic Programming ◽  
Engineering Applications ◽  
Context Free Grammar ◽  
Multigene Genetic Programming ◽  
Context Free

The GP method explained in previous chapters was about the evolution of computer programs represented by monolithic gene (syntax tree). This is the original and most widespread type of GP that is also referred to as tree-based GP. In recent years, new variants of GP have emerged that follow the basic idea of traditional GP to automatically evolve computer programs, but the programs are evolved/represented in different ways. New variants of GP include but are not limited to stack-based genetic programming, linear genetic programming (LGP), Cartesian genetic programming, grammatical evolution (GE), graph-based GP (GGP), context-free grammar (CFGGP), multigene genetic programming (MGGP), and gene expression programming (GEP). Among these variants, main features, evolution of computer programs, and a brief review of engineering applications of MGGP, GEP, and LGP are introduced in this chapter.

Gene Expression Programming and the Evolution of Computer Programs

2011 ◽  
pp. 2154-2173
Author(s):  
Cândida Ferreira
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Genetic Algorithms ◽  
Genetic Programming ◽  
Gene Expression Programming ◽  
Search Space ◽  
Computer Programs ◽  
Problem Solver

In this chapter an artificial problem solver inspired in natural genotype/phenotype systems — gene expression programming — is presented. As an introduction, the fundamental differences between gene expression programming and its predecessors, genetic algorithms and genetic programming, are briefly summarized so that the evolutionary advantages of gene expression programming are better understood. The work proceeds with a detailed description of the architecture of the main players of this new algorithm (chromosomes and expression trees), focusing mainly on the interactions between them and how the simple yet revolutionary structure of the chromosomes allows the efficient, unconstrained exploration of the search space. And finally, the chapter closes with an advanced application in which gene expression programming is used to evolve computer programs for diagnosing breast cancer.

Study on the Optimize Strategies of Gene Expression Programming

2013 ◽  
Vol 432 ◽  
pp. 565-570
Author(s):  
Xin Wen Gao ◽  
Ben Bo Guan ◽  
Xing Jian Guan
Keyword(s):  
Gene Expression ◽  
Genetic Algorithm ◽  
Variable Selection ◽  
Genetic Programming ◽  
Evolutionary Computation ◽  
Local Convergence ◽  
Gene Expression Programming ◽  
Optimal Solution ◽  
Fitness Calculation ◽  
Selection Of

The purpose of this paper is to improve the efficiency of the Gene Expression Programming (GEP) algorithm. The GEP algorithm is an evolutionary computation. It inherits the characteristics of Genetic Algorithm and Genetic Programming. Through its own characteristics, the GEP algorithm can get the optimal solution of the complicated problem. So, the GEP algorithm has achieved good results in many areas. However, there are also some inevitable drawbacks about the GEP algorithm itself. This paper proposes 5 deficiencies aspects of the GEP algorithm (expression meaning, fitness calculation, local convergence, variable selection, genetic operations, selection of genetic operation rates), and gives the corresponding solutions.

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.

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