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.

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.

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.

Gene Expression Programming and the Evolution of Computer Programs

2005 ◽  
pp. 82-103 ◽  
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.

scholarly journals A NEW LINEAR GENETIC PROGRAMMING APPROACH BASED ON STRAIGHT LINE PROGRAMS: SOME THEORETICAL AND EXPERIMENTAL ASPECTS

2009 ◽  
Vol 18 (05) ◽  
pp. 757-781 ◽  
Author(s):  
CÉSAR L. ALONSO ◽  
JOSÉ LUIS MONTAÑA ◽  
JORGE PUENTE ◽  
CRUZ ENRIQUE BORGES
Keyword(s):  
Data Structure ◽  
Genetic Programming ◽  
Computer Programs ◽  
Symbolic Regression ◽  
Programming Approach ◽  
Linear Genetic Programming ◽  
Straight Line ◽  
Structured Representations ◽  
Regression Problems ◽  
Straight Line Programs

Tree encodings of programs are well known for their representative power and are used very often in Genetic Programming. In this paper we experiment with a new data structure, named straight line program (slp), to represent computer programs. The main features of this structure are described, new recombination operators for GP related to slp's are introduced and a study of the Vapnik-Chervonenkis dimension of families of slp's is done. Experiments have been performed on symbolic regression problems. Results are encouraging and suggest that the GP approach based on slp's consistently outperforms conventional GP based on tree structured representations.

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.

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