NDLI: Genetic Programming

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Genetic Programming

Content Provider	Scilit
Author	Vasuki, A.
Copyright Year	2020
Description	Genetic programming (GP) belongs to the family of evolutionary computational algorithms that can be applied to problems which are difficult to solve with the traditional methods. Friedberg pioneered the work on evolutionary algorithms in 1958 from which genetic programming evolved. The genetic programming technique was proposed by John R. Koza [1] in 1989 and is an extension of the genetic algorithm (GA). Evolutionary algorithms try to mimic the biological process and are based on the Darwinian principle of survival of the fittest. Evolutionary algorithms could be applied to problems where heuristic techniques might not produce optimum results. They are suitable for solving practical problems in several domains [2]. Evolution occurs with survival of the fittest in populations that compete for existing natural resources. The fit individuals contribute more to the process of reproduction, and hence they are more likely to be members of or produce offspring for the next generation. Evolutionary algorithms are suitable for optimizing unimodal as well as multimodal functions and are simple and easy to implement. Book Name: Nature-Inspired Optimization Algorithms
Related Links	https://content.taylorfrancis.com/books/download?dac=C2019-0-98738-8&isbn=9780429289071&doi=10.1201/9780429289071-5&format=pdf
Ending Page	76
Page Count	16
Starting Page	61
DOI	10.1201/9780429289071-5
Language	English
Publisher	Informa UK Limited
Publisher Date	2020-05-31
Access Restriction	Open
Subject Keyword	Book Name: Nature-inspired Optimization Algorithms History and Philosophy of Science Survival Evolutionary Algorithms Functions Optimizing Solving Fittest
Content Type	Text
Resource Type	Chapter

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