Accelerated image halftoning technique using improved genetic algorithm

This paper presents an accelerated image halftoning technique using an impr oved genetic algorithm with tiny populations. The algorithm is based on a n ea cooperative model for genetic operators in GA. Two kinds of operators ar e used in parallel to produce offspring: (i) SRM (Self-Reproduction with Mu tation) to introduce diversity by means of Adaptive Dynamic-Block (ADB) mut ation inducing the appearance of beneficial mutations. (ii) CM (Crossover a nd Mutation) to promote the increase of beneficial mutations in the populat ion SRM applies qualitative mutation only to the bits inside a mutation blo ck and controls the required exploration-exploitation balance through its a daptive mechanism. An extinctive selection mechanism subjects SRM's and CM' s offspring to compete for survival. The simulation results show that our s cheme impressively reduces computer memory and processing time required to obtain high quality halftone images. For example, compared to the conventio nal image halftoning technique with GA, the proposed algorithm using only a 2% population size required about 15% evaluations to generate high quality images. The results make our scheme appealing for practical implementation s of the image halftoning technique using GA.