Optimized Genetic Programming Applications

Ali Danandeh Mehr, Bahrudin Hrnjica

ISBN 10: 152256005X / ISBN 13: 9781522560050

Published by IGI Global, US, 2018

About this Item

Data is more valuable than ever in the twenty-first century, and tremendous amounts of data are being generated every second. With a fast-growing information industry, engineers are required to develop new tools and techniques that increase human capabilities of mining useful knowledge from the vast amounts of data.Optimized Genetic Programming Applications: Emerging Research and Opportunities is an essential reference source that explores the concept of genetic programming and its role in managing engineering problems. It also examines genetic programming as a supervised machine learning technique, focusing on implementation and application. As a resource that details both the theoretical aspects and implementation of genetic programming, this book is a useful source for academicians, biological engineers, computer programmers, scientists, researchers, and upper-level students seeking the latest research on genetic programming.

Seller Inventory # LU-9781522560050

Bibliographic Details

• Title: Optimized Genetic Programming Applications
• Publisher: IGI Global, US
• Publication Date: 2018
• Language: English
• ISBN 10: 152256005X
• ISBN 13: 9781522560050
• Binding: Hardback
• Edition: 1st.
• Condition: New
• Dimensions: 17.78W x 1.91H x 25.4D centimeters 17.78 centimeters width by 1.91 centimeters height by 25.4 centimeters depth
• Weight: 825 grams

About this title

Synopsis

Data is more valuable than ever in the twenty-first century, and tremendous amounts of data are being generated every second. With a fast-growing information industry, engineers are required to develop new tools and techniques that increase human capabilities of mining useful knowledge from the vast amounts of data. Optimized Genetic Programming Applications: Emerging Research and Opportunities is an essential reference source that explores the concept of genetic programming and its role in managing engineering problems. It also examines genetic programming as a supervised machine learning technique, focusing on implementation and application. As a resource that details both the theoretical aspects and implementation of genetic programming, this book is a useful source for academicians, biological engineers, computer programmers, scientists, researchers, and upper-level students seeking the latest research on genetic programming.