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. 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. 

Provides step-by-step tutorials and program codes Presents GA applications for single-objective and multi-objective optimization in connection with nonlinear system modeling and distributed parameter system modeling Proposes control optimization methods from traditional PID controllers and advanced model predictive controls to advanced fuzzy controllers Introduces the newest topics in deep learning and CNN

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