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Fundamentals of Genetic Programming

Fundamentals of Genetic Programming

Copyright: © 2019 |Pages: 47
ISBN13: 9781522560050|ISBN10: 152256005X|EISBN13: 9781522560067
DOI: 10.4018/978-1-5225-6005-0.ch001
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MLA

Bahrudin Hrnjica and Ali Danandeh Mehr. "Fundamentals of Genetic Programming." Optimized Genetic Programming Applications: Emerging Research and Opportunities, IGI Global, 2019, pp.1-47. https://doi.org/10.4018/978-1-5225-6005-0.ch001

APA

B. Hrnjica & A. Danandeh Mehr (2019). Fundamentals of Genetic Programming. IGI Global. https://doi.org/10.4018/978-1-5225-6005-0.ch001

Chicago

Bahrudin Hrnjica and Ali Danandeh Mehr. "Fundamentals of Genetic Programming." In Optimized Genetic Programming Applications: Emerging Research and Opportunities. Hershey, PA: IGI Global, 2019. https://doi.org/10.4018/978-1-5225-6005-0.ch001

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Abstract

In the living world, all species share one very important property, they receive it right after the birth, and it is called the survival instinct. Since the middle of the twentieth century, scientists have been applying the phenomenon in engineering in order to define computer algorithms which follow the principles of biological evolution of species. Eighty years later, scientists and engineers are still applying the phenomenon in order to solve today's most complex and wide variety of problems. This chapter introduces evolutionary algorithms and motivates the reader to start a journey into genetic programming (GP). The chapter starts with the introduction and detailed insights into GP by describing its main parts and terminology in order to define and mimic biological terms with terms in genetic programming. Then the reader is introduced with the historical evolution of GP and the main and the most popular genetic programming variants, it may find dozens of cited references in it. The chapter continues with detailed introduction on the chromosomes, population, initial and selection methods, main genetic operators, various types of fitness functions, termination criteria, etc. Since GP is processor intensive algorithm, it requires parallel execution to increase its performance which is described at the end of the chapter.

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