1. Halftoning Technique Using Genetic Algorithm Naoki Kobayashi and Hideo Saito 1994 IEEE

2. The Flow of GA

3. Halftoning Technique using Genetic Algorithm There are two proposed methods for halftoning techniques using GA.

4. Method I-Initial Population The initial population of n strings such as the sample is produced randomly and independently of the gray-tone block.

5. Method I-fitness value b g (i,j) is a convoluted b(i,j) by the gaussian filter. g s (I,j) is the convoluted g(I,j) by the smoothing filter.

6. Method I-fitness value(cont.)

7. Method I-Reproduction The population size is n and the fitness value of the ith string is F(i), a selection ’ s probability P(i) of ith string

8. Method I-Crossover One of the two crossover methods is selected randomly.

9. Method I-Mutation The string is selected randomly and one pixel in the string is inverted. The black changes to the white or the white changes to the black.

10. Method II-Initial Population The N b black pixels

11. Method II-Fitness Value The calculation is the same as it in the method I.

12. Method II-Reproduction The calculation is the same as it in the method I.

13. Method II-Crossover The number of black pixels between tow strings are changed when the pixels in the local region are exchanged, some pixels are randomly selected and then exchanged between two strings for making the number of black pixels even.

14. Method II-Mutation The string is selected randomly, two pixels in the string is selected randomly and the two pixels are exchanged.

15. Conclusion Using the proposed method, visually pleasing halftone images were obtained. However, the computation time was long in this method.