Optimization of Different Fitness Functions Using Reproduction, Crossover and Mutation of Genetic Algorithm With Binary Number Scheduling

Genetic algorithms are search algorithms based on the mechanics of natural selection and natural genetics. In this paper, we investigate a novel approach to the binary coded testing process based on a genetic algorithm. This paper consists of two parts. Thefirst part addresses the problem in the traditional way of using the decimal number system to define the fitness function to study the variations of counts and the variations of probability against the fitness functions. Second, the initialpopulationsare defined using binary coded digits (genes). For the evaluation of the high fitness function values,three genetic operators, namely, reproduction, crossover and mutation, are randomly used. The results show the importance of the genetic operator, mutation, which yields the peak values for the fitness function based on binary coded numbers performed in a new way.

Study on Optical Constants of DLC Thin Films Based on Modified Genetic Algorithm

This document modified the basic genetic algorithm three important operator—selection operator, mutation operator, crossover operator drawing from competition, selection, and fitness niche adjustment idea. Take the unbalanced magnetron sputtering deposition of DLC films on Si substrate as a research sample. When fitting wavelength range is 300 ~ 1500nm, select a point every 1nm, the fitting lasting time is no more than 6s. Fitting results contrast to ellipsometer’s fitting results; the error is less than 2%. Experiments show that: the adaptive modified is effective in preventing the "premature" phenomenon, searched the global optimal, reduces the requirements on the operator to set the initial value on the fitted model and is effectiveness to fit thin film optical constants.