The Genetic Algorithm is a popular optimization technique which is bio-inspired and is based on the concepts of natural genetics and natural selection theories proposed by Charles Darwin. The Algorithm functions on three basic genetic operators of selection, crossover and mutation. Based on the types of these operators GA has many variants like Real coded GA, Binary coded GA, Sawtooth GA, Micro GA, Improved GA, Differential Evolution GA. This paper discusses a few of the forms of GA and applies the techniques to the problem of Function optimization and System Identification. The paper makes a comparative analysis of the advantages and disadvantages of the different types of GA. The computer simulations illustrate the results. It also makes a comparison between the GA technique and Incremental LMS algorithm for System Identification. ududud
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机译：遗传算法是一种受生物启发的流行优化技术，它基于查尔斯·达尔文提出的自然遗传学和自然选择理论。该算法对选择，交叉和变异的三个基本遗传算子起作用。根据这些运算符的类型，GA具有许多变体，例如，实数编码GA，二值编码GA，锯齿形GA，微型GA，改进型GA，差分演化GA。本文讨论了几种遗传算法形式，并将这些技术应用于功能优化和系统识别问题。本文对不同类型的GA的优缺点进行了比较分析。计算机仿真说明了结果。它还比较了GA技术和用于系统识别的增量LMS算法。 ud ud ud
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