A survey of mutation techniques in genetic programming
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paper_title: Artificial Intelligence: Genetic Programming

paper_content:

This article discusses the method of genetic programming by first setting it into the context of artificial intelligence and the theory of evolution, and then by describing its mechanisms and the progress of the field in recent years. The second part of this article explains applications of the method. The article starts with a general discussion, then points out some of the striking results of the method, and finally turns to the area close to social and behavioral sciences. The article concludes with a discussion of methodological issues and perspectives.
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Title: A Survey of Mutation Techniques in Genetic Programming
Section 1: INTRODUCTION
Description 1: Introduce the concept of genetic programming (GP), the role of mutation in GP, and the importance of mutation in various evolutionary computation fields.

Section 2: BACKGROUND
Description 2: Provide an overview of previous research and surveys on GP, focusing specifically on mutation, and outline the common features of mutation techniques in GP.

Section 3: DISCUSSION
Description 3: Discuss the incomplete reporting of mutation parameters in research, propose a candidate tableau for mutation reporting, and present a mutation reference organization for GP.

Section 4: Mutation characteristic Options
Description 4: List and describe various options for mutation characteristics including individual selection, mutation rate, node bias, mutation type, mutation suboperation, depth limit, ramp, and mutation retry.

Section 5: CONCLUSION
Description 5: Summarize the importance of understanding mutation in GP, discuss the variety of mutation techniques and their advantages, and propose guidance for future mutation research and reporting.