£104.91

Springer DNA Computing Based Genetic Algorithm Book

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Description

Explore the advanced integration of DNA and RNA-based genetic algorithms within industrial process modeling and control. This specialized text from Springer provides a deep look into how these biological computing methods connect with neural network modeling, fuzzy control, and the Q-learning algorithm. It is an essential resource for those studying the implementation of CNN deep learning classifiers and complex optimization techniques. Readers will find detailed information on various DNA/RNA-based genetic algorithms and their modifications. The content moves beyond theory by testing these methods against benchmarks and providing specific implementation steps along with program code. Whether you are focusing on single-objective optimization or solving multi-objective optimization problems for neural network modeling and fuzzy control, this book offers the technical foundation required for modern computational research.

Key Features

Covers DNA/RNA-based genetic algorithms in connection with neural network modeling and fuzzy control systems.

Includes technical details on the Q-learning algorithm and CNN deep learning classifier applications.

Provides practical implementation steps and program code to assist with real-world application.

Discusses both single-objective and multi-objective optimization for complex industrial modeling.

Features various DNA/RNA-based genetic algorithms that are tested using established benchmarks.

Product Specifications

Format
Hardcover
Domain
Amazon UK
Release Date
01 July 2020
Listed Since
08 April 2020

Barcode

No barcode data available

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