DNA Computing Based Genetic Algorithm: Applications in Industrial Process Modeling and ControlКНИГИ » ПРОГРАММИНГ
Название: DNA Computing Based Genetic Algorithm: Applications in Industrial Process Modeling and Control Автор: Jili Tao, Ridong Zhang, Yong Zhu Издательство: Springer Год: 2020 Страниц: 280 Язык: английский Формат: pdf (true) Размер: 25.1 MB
Provides step-by-step tutorials and program codes.
This book focuses on the implementation, evaluation and application of DNA/RNA-based genetic algorithms in connection with neural network modeling, fuzzy control, the Q-learning algorithm and CNN deep learning classifier. It presents several DNA/RNA-based genetic algorithms and their modifications, which are tested using benchmarks, as well as detailed information on the implementation steps and program code. In addition to single-objective optimization, here genetic algorithms are also used to solve multi-objective optimization for neural network modeling, fuzzy control, model predictive control and PID control. In closing, new topics such as Q-learning and CNN are introduced.
Genetic algorithms (GAs) are one of the evolutionary computing techniques, which have been widely used to solve complex optimization problems that are known to be difficult for traditional calculus-based optimization techniques. These traditional optimization methods generally require the problem to possess certain mathematical properties, such as continuity, differentiability, and convexity, which cannot be satisfied in many practical problems. As such, GA, that does not require these requirements, has been considered and often adopted as an efficient optimization tool in many applications.
The book offers a valuable reference guide for researchers and designers in system modeling and control, and for senior undergraduate and graduate students at colleges and universities.
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DNA Computing Based Genetic Algorithm: Applications in Industrial Process Modeling and Control 
Название: DNA Computing Based Genetic Algorithm: Applications in Industrial Process Modeling and Control 
