Research Anthology on Multi-Industry Uses of Genetic Programming and Algorithms (3 Volumes)

Research Anthology on Multi-Industry Uses of Genetic Programming and Algorithms (3 Volumes)

Release Date: December, 2020|Copyright: © 2021 |Pages: 1534
DOI: 10.4018/978-1-7998-8048-6
ISBN13: 9781799880486|ISBN10: 1799880486|EISBN13: 9781799880998
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Description & Coverage
Description:

Genetic programming is a new and evolutionary method that has become a novel area of research within artificial intelligence known for automatically generating high-quality solutions to optimization and search problems. This automatic aspect of the algorithms and the mimicking of natural selection and genetics makes genetic programming an intelligent component of problem solving that is highly regarded for its efficiency and vast capabilities. With the ability to be modified and adapted, easily distributed, and effective in large-scale/wide variety of problems, genetic algorithms and programming can be utilized in many diverse industries. This multi-industry uses vary from finance and economics to business and management all the way to healthcare and the sciences. The use of genetic programming and algorithms goes beyond human capabilities, enhancing the business and processes of various essential industries and improving functionality along the way.

The Research Anthology on Multi-Industry Uses of Genetic Programming and Algorithms covers the implementation, tools and technologies, and impact on society that genetic programming and algorithms have had throughout multiple industries. By taking a multi-industry approach, this book covers the fundamentals of genetic programming through its technological benefits and challenges along with the latest advancements and future outlooks for computer science. This book is ideal for academicians, biological engineers, computer programmers, scientists, researchers, and upper-level students seeking the latest research on genetic programming.

Coverage:

The many academic areas covered in this publication include, but are not limited to:

  • Artificial Intelligence
  • Classification
  • Computational Models
  • Computer Science
  • Crossover
  • Genetic Algorithms
  • Genetic Programming
  • Machine Learning
  • Mutation
  • Optimization
  • Replication
  • Selection
Table of Contents
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