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When the linear array image sensor (CCD) is used for spot detection, the optimization of the detection signal is usually one of the problems that plague the user. In linear array imaging sensor (CCD) detection applications, optimization of the detection signal is usually one of the problems with the user. Based on the characteristics of linear array CCD detection signal, a genetic algorithm (GA) is established to solve the problem of mathematical model. In this paper, a new adaptive genetic algorithm (IGA) with directional adaptive guidance and adaptive control technology and threshold constraint technology are proposed for the lack of local optimization ability of the standard genetic algorithm (SGA), premature convergence and low accuracy. By applying IGA to the actual detection data, it is proved that IGA has certain advantages in solving the problem of linear CCD detection signal optimization. In the end of this paper, the performance of IGA, SGA and peak finding algorithm (SP) are analyzed and compared, which fully demonstrates the advantages of IGA in solving such problems.

The application research on improvement of genetic algorithm in linear CCD detection