Earticle

초록

영어

Genetic programming (GP) is an automated method for creating a working computer program from a high-level problem statement of a problem. Genetic programming starts from a high-level statement of “what needs to be done” and automatically creates a computer program to solve the problem. In artificial intelligence, genetic programming (GP) is an evolutionary algorithm-based methodology inspired by biological evolution to find computer programs that perform a user defined task. It is a specialization of genetic algorithms (GA) where each individual is a computer program. It is a machine learning technique used to optimize a population of computer programs according to a fitness span determined by a program's ability to perform a given computational task. This paper presents a idea of the various principles of genetic programming which includes, relative effectiveness of mutation, crossover, breeding computer programs and fitness test in genetic programming. The literature of traditional genetic algorithms contains related studies, but through GP, it saves time by freeing the human from having to design complex algorithms. Not only designing the algorithms but creating ones that give optimal solutions than
traditional counterparts in noteworthy ways.

목차

Abstract
 I. Introduction
 II. The Crossover (Recombination) Operation
 III. Mutation In Nature
 IV. Applications of Genetic Programming
 V. Conclusion
 VI. References

키워드

저자

• T. Venkat Narayana Rao [ Professor and Head, Computer Science and Engineering Hyderabad Institute of Technology and Management, Hyderabad, A P, India ]
• Srikanth Madiraju [ Computer Science and Engineering, Hyderabad Institute of Technology and Management, Hyderabad, A P, India ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Hybrid Information Technology

• 간기

  격월간

• pISSN

  1738-9968

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605