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.

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.

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.

An Intelligent Process Development Using Fusion of Genetic Algorithm with Fuzzy Logic

2015 ◽  
pp. 44-81 ◽  
Author(s):  
Kunjal Bharatkumar Mankad
Keyword(s):  
Genetic Algorithm ◽  
Process Development ◽  
Intelligent System ◽  
Mathematical Formulation ◽  
Research Work ◽  
Fuzzy Rule ◽  
Genetic Operators ◽  
Rule Based System ◽  
Intelligent Process

Intelligent System (IS) can be defined as the system that incorporates intelligence into applications being handled by machines. The chapter extensively discusses the role of Genetic Algorithm (GA) in the search and optimization process along with discussing applications developed so far. A very detailed discussion on the Fuzzy Rule-Based System is presented along with major applications developed in different domains. The chapter presents algorithm of implementing intelligent procedure to decide whether a patient is prone to heart disease or not. The procedure evolves solutions using genetic operators and provides its decision automatically. The chapter presents discussion on the results achieved as a result of prototypical implementation of the evolutionary fuzzy hybrid model. The significant advantage of the presented research work is that applications that do not have any mathematical formulation and still demand optimization can be easily solved using the designed approach.

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.

scholarly journals Applicability of Cellular Automata in Cryptanalysis

Cryptography ◽  
2020 ◽  
pp. 180-191
Author(s):  
Harsh Bhasin ◽  
Naved Alam
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Cellular Automata ◽  
Literature Review ◽  
Soft Computing ◽  
Search Problem ◽  
Extensive Literature ◽  
Soft Computing Techniques ◽  
A New Technique ◽  
The Given

Cryptanalysis refers to finding the plaintext from the given cipher text. The problem reduces to finding the correct key from a set of possible keys, which is basically a search problem. Many researchers have put in a lot of effort to accomplish this task. Most of the efforts used conventional techniques. However, soft computing techniques like Genetic Algorithms are generally good in optimized search, though the applicability of such techniques to cryptanalysis is still a contentious point. This work carries out an extensive literature review of the cryptanalysis techniques, finds the gaps there in, in order to put the proposed technique in the perspective. The work also finds the applicability of Cellular Automata in cryptanalysis. A new technique has been proposed and verified for texts of around 1000 words. Each text is encrypted 10 times and then decrypted using the proposed technique. The work has also been compared with that employing Genetic Algorithm. The experiments carried out prove the veracity of the technique and paves way of Cellular automata in cryptanalysis. The paper also discusses the future scope of the work.

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 Optimal Design of the Cement, Fly Ash, and Slag Mixture in Ternary Blended Concrete Based on Gene Expression Programming and the Genetic Algorithm

Materials ◽  
2019 ◽  
Vol 12 (15) ◽  
pp. 2448 ◽  
Author(s):  
Wang
Keyword(s):  
Gene Expression ◽  
Genetic Algorithm ◽  
Fly Ash ◽  
Gene Expression Programming ◽  
Design Strength ◽  
Carbon Pricing ◽  
Total Cost ◽  
Material Cost ◽  
Wide Range ◽  
Ternary Blended Concrete

Concrete producers and construction companies are interested in improving the sustainability of concrete, including reducing its CO2 emissions and the costs of materials while maintaining its mechanical properties, workability, and durability. In this study, we propose a simple approach to the optimal design of the fly ash and slag mixture in blended concrete that considers the carbon pricing, material cost, strength, workability, and carbonation durability. Firstly, the carbon pricing and the material cost are calculated based on the concrete mixture and unit prices. The total cost equals the sum of the material cost and the carbon pricing, and is set as the optimization’s objective function. Secondly, 25 various mixtures are used as a database of optimization. The database covered a wide range of strengths between 25 MPa and 55 MPa and a wide range of workability between 5 and 25 cm in slump. Gene expression programming is used to predict the concrete’s strength and slump. The ternary blended concrete’s carbonation depth is calculated using the efficiency factors of fly ash and slag. Thirdly, the genetic algorithm is used to find the optimal mixture under various constraints. We provide examples to illustrate the design of ternary blended concrete with different strength levels and environmental CO2 concentrations. The results show that, for a suburban region, carbonation durability is the controlling factor in terms of the design of the mixture when the design strength is less than 40.49 MPa, and the compressive strength is the controlling factor in the design of the mixture when the design strength is greater than 40.49 MPa. For an urban region, the critical strength for distinguishing carbonation durability control and strength control is 45.93 MPa. The total cost, material cost, and carbon pricing increase as the concrete’s strength increases.

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