scholarly journals A Comparative Study on the Performance of Gene Expression Programming and Machine Learning Methods

2021 ◽  
pp. 140-147
Author(s):  
Mohammad Anas ◽  
Mohiuddeen Khan ◽  
Hammad Basit
Keyword(s):  
Gene Expression ◽  
Performance Metrics ◽  
Polynomial Regression ◽  
Gene Expression Programming ◽  
Research Work ◽  
Living Organism ◽  
Computer Programs ◽  
Test Accuracy ◽  
Tree Structures ◽  
High Bias

Usually, evolutionary algorithms are used to provide strong approximations to problems that are difficult to solve with other methods. Gene expression programming (GEP) is a type of evolutionary algorithm used in computer programming to generate computer programs or models. These computer programs are complex tree structures that, like a living organism, learn and adapt by modifying their sizes, shapes, and composition. In the present work, a comparison study was made among GEP and the standard prediction techniques to find the best predicting model on the BOSTON HOUSING dataset. Three approaches viz. GEP, ANN and polynomial regression were implemented on the dataset. The study showed how the three methods solve the problem of high bias and high variance and which one outperforms the other. The research work, however, gave a glimpse of the actual limitations and advantages of the methods on one another indicating the dependency of method on the type of data used. The results conclude the comparison of different methods on different performance metrics. The GEP model however reduced the problem of high bias and high variance by giving a slight difference between the train and test accuracy but was not able to outperform ANN and polynomial regression in terms of performance metrics.

scholarly journals Application of Gene Expression Programming, Artificial Neural Network and Multilinear Regression in Predicting Hydrochar Physicochemical Properties

2020 ◽  
Author(s):  
Jibril Abdulsalam ◽  
Abiodun Ismail Lawal ◽  
Ramadimetja Lizah Setsepu ◽  
Moshood Onifade ◽  
Samson Bada
Keyword(s):  
Neural Network ◽  
Gene Expression ◽  
Artificial Neural Network ◽  
Performance Metrics ◽  
Gene Expression Programming ◽  
Mean Bias Error ◽  
Energy Yield ◽  
Multilinear Regression ◽  
Soft Computing Techniques ◽  
Artificial Neural

Abstract Globally, the provision of energy is becoming an absolute necessity. Biomass resources are abundant and have been described as a potential alternative source of energy. However, it is important to assess the fuel characteristics of the various available biomass sources. Soft computing techniques are presented in this study to predict the mass yield (MY), energy yield (EY), and higher heating value (HHV) of hydrothermally carbonized biomass by using Gene Expression Programming (GEP), multiple-input single output-artificial neural network (MISO-ANN), and Multilinear regression (MLR). The three techniques were compared using statistical performance metrics. The coefficient of determination (R2), mean absolute error (MAE), and mean bias error (MBE) were used to evaluate the performance of the models. The MISO-ANN with 5-10-10-1 and 5-15-15-1 network architectures provided the most satisfactory performance of the three proposed models (R2 = 0.976, 0.955, 0.996; MAE = 2.24, 2.11, 0.93; MBE = 0.16, 0.37, 0.12) for MY, EY and HHV respectively. The GEP technique’s ability to predict hydrochar properties based on the input parameters was found to be satisfactory, while MLR provided an unsatisfactory predictive model. Sensitivity analysis was conducted, and the analysis revealed that volatile matter (VM) and temperature (Temp) have more influence on the MY, EY, and HHV.

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.

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.

scholarly journals Application of gene expression programming, artificial neural network and multilinear regression in predicting hydrochar physicochemical properties

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Jibril Abdulsalam ◽  
Abiodun Ismail Lawal ◽  
Ramadimetja Lizah Setsepu ◽  
Moshood Onifade ◽  
Samson Bada
Keyword(s):  
Neural Network ◽  
Gene Expression ◽  
Artificial Neural Network ◽  
Performance Metrics ◽  
Gene Expression Programming ◽  
Mean Bias Error ◽  
Energy Yield ◽  
Multilinear Regression ◽  
Soft Computing Techniques ◽  
Artificial Neural

AbstractGlobally, the provision of energy is becoming an absolute necessity. Biomass resources are abundant and have been described as a potential alternative source of energy. However, it is important to assess the fuel characteristics of the various available biomass sources. Soft computing techniques are presented in this study to predict the mass yield (MY), energy yield (EY), and higher heating value (HHV) of hydrothermally carbonized biomass using Gene Expression Programming (GEP), multiple-input single output-artificial neural network (MISO-ANN), and Multilinear regression (MLR). The three techniques were compared using statistical performance metrics. The coefficient of determination (R2), mean absolute error (MAE) and mean bias error (MBE) were used to evaluate the performance of the models. The MISO-ANN with 5-10 to 10-1 and 5-15-15-1 network architectures provided the most satisfactory performance of the three proposed models (R2 = 0.976, 0.955, 0.996; MAE = 2.24, 2.11, 0.93; MBE = 0.16, 0.37, 0.12) for MY, EY and HHV, respectively. The GEP technique’s ability to predict hydrochar properties based on the input parameters was found to be satisfactory, while MLR provided an unsatisfactory predictive model. Sensitivity analysis was conducted, and the analysis revealed that volatile matter (VM) and temperature (Temp) have more influence on the MY, EY, and HHV.

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.

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