| Summary: | 碩士 === 中原大學 === 電機工程研究所 === 91 === Genetic Algorithms (GA) as a tool for a search and optimization methodology has now reached a mature stage. The GA works on the Darwinian principle of natural selection for which the noted English philosopher coined the phrase ”Survival of the fittest”. GA is not mathematically oriented. Unlike some traditional approaches the idea of using a population of solution to solve practical engineering optimization problems [7]. In this text, MATLAB and SIMULINK are used a technical computing environment for high-performance numerical computation and visualization. MATLAB and SIMULINK integrate numerical analysis, matrix computation, signal processing, and graphics in an easy-to-use environment without tradition programming. The several toolboxes provide many predefined function that can be called by the user to simulate various types, respectively. Genetic Algorithms has emerged as a powerful tool PID controller. That unlike the traditional is aimed at an accommodation with the pervasive imprecision of the real world [8]. Thus, the guiding principle is to exploit the tolerance for uncertainty, partial truth to achieve tractability, robustness, and low solution cost with reality. In the final, the role design has proven to be effective in practical industrial design.
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