Gene Expression Programming

Gene expression programming, often known as GEP, is an evolutionary technique used in the field of computer programming that is used to construct computer programs or models. These computer programs take the form of complex tree structures that, in a manner analogous to that of real organisms, are able to learn and adapt by altering their sizes, forms, and composition. In addition, just like the chromosomes of real creatures, the GEP computer programs are encoded in simple linear chromosomes that are a predetermined length. GEP is an example of a genotype-phenotype system since it possesses both a simple genome for the purpose of storing and transmitting genetic information and a complex phenotype for the purpose of exploring the environment and adapting to it.

How You Will Benefit

(I) Insights, and validations about the following topics:

Chapter 1: Gene expression programming

Chapter 2: Symbolic regression

Chapter 3: Decision tree

Chapter 4: Evolutionary algorithm

Chapter 5: Genetic algorithm

Chapter 6: Genetic programming

Chapter 7: Grammatical evolution

Chapter 8: Artificial intelligence

Chapter 9: Machine learning

Chapter 10: Artificial neural network

(II) Answering the public top questions about gene expression programming.

(III) Real world examples for the usage of gene expression programming in many fields.

Who This Book Is For

Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of gene expression programming.

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