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Genetic algorithms and applications ...

Kapoor, Vivek, (1974-)

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Genetic algorithms and applications for stock trading optimization

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Genetic algorithms and applications for stock trading optimization/ by Vivek Kapoor and Shubhamoy Dey.
作者:	Kapoor, Vivek,
其他作者:	Dey, Shubhamoy,
出版者:	Hershey, Pennsylvania :IGI Global, : 2021.,
面頁冊數:	1 online resource (x, 262 p.)
內容註:	Section 1. Genetic algorithms, neural networks, and chaos theory. Chapter 1. Introduction to expert systems, fuzzy logic, neural networks, and chaos theory ; Chapter 2. Introduction to biologically inspired algorithms ; Chapter 3. Introduction to genetic algorithms in search and optimization ; Chapter 4. Genetic algorithms (GAs) and their mathematical foundations -- Section 2. Genetic algorithms theory and its working. Chapter 5. Genetic algorithm (GA) methodology andits internal working ; Chapter 6. Understanding genetic algorithm (GA) operators step by step ; Chapter 7. Operator control parameters and fine tuning of genetic algorithms (GAs) ; Chapter 8. Advance GA operators and techniques in search and optimization -- Section 3. Genetic algorithms in finance. Chapter 9. Genetic algorithms (GAs) and stock trading systems ; Chapter 10. Synergistic market analysis, technical analysis, and various indicators ; Chapter 11. Using genetic algorithms to develop investment strategies ; Chapter 12. Developing a single indicator or multiple indicator market timing system -- Section 4. Genetic algorithms in other areas. Chapter 13. Some other applications of genetic algorithms (GAs);Chapter 14. Introduction to some other nature-inspired algorithms.
標題:	Stocks - Mathematical models. -
電子資源:	https://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/978-1-7998-4105-0
ISBN:	9781799841067

Genetic algorithms and applications for stock trading optimization
Kapoor, Vivek,1974-

Genetic algorithms and applications for stock trading optimization[electronic resource] /by Vivek Kapoor and Shubhamoy Dey. - Hershey, Pennsylvania :IGI Global,2021. - 1 online resource (x, 262 p.)

Includes bibliographical references and index.

Section 1. Genetic algorithms, neural networks, and chaos theory. Chapter 1. Introduction to expert systems, fuzzy logic, neural networks, and chaos theory ; Chapter 2. Introduction to biologically inspired algorithms ; Chapter 3. Introduction to genetic algorithms in search and optimization ; Chapter 4. Genetic algorithms (GAs) and their mathematical foundations -- Section 2. Genetic algorithms theory and its working. Chapter 5. Genetic algorithm (GA) methodology andits internal working ; Chapter 6. Understanding genetic algorithm (GA) operators step by step ; Chapter 7. Operator control parameters and fine tuning of genetic algorithms (GAs) ; Chapter 8. Advance GA operators and techniques in search and optimization -- Section 3. Genetic algorithms in finance. Chapter 9. Genetic algorithms (GAs) and stock trading systems ; Chapter 10. Synergistic market analysis, technical analysis, and various indicators ; Chapter 11. Using genetic algorithms to develop investment strategies ; Chapter 12. Developing a single indicator or multiple indicator market timing system -- Section 4. Genetic algorithms in other areas. Chapter 13. Some other applications of genetic algorithms (GAs);Chapter 14. Introduction to some other nature-inspired algorithms.

"This book offers an overall general review of internal working of Genetic Algorithms (GAs) in search and optimization, and their use in to find out attractive stock trading strategies"--

Mode of access: World Wide Web.

ISBN: 9781799841067Subjects--Topical Terms:

670141
Stocks
--Mathematical models.Subjects--Index Terms:

Biologically Inspired Algorithms.Index Terms--Genre/Form:

542853
Electronic books.

LC Class. No.: HG4661 / .K37 2021eb

Dewey Class. No.: 332.64/201519625

Genetic algorithms and applications for stock trading optimization
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504 $a Includes bibliographical references and index.
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653 $a Search Optimization.
653 $a Stock Trading Systems.
653 $a Synergistic Market Analysis.
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