Genetic algorithms (GAs) have been used as a powerful tool for cryptanalyzing affine ciphers in this paper for the first time. They are one of heuristic search techniques which use natural selection. They select the optimal solution by using selection, crossover and mutation operations. The useful parameters in GAs are kept in the memory and the best values of fitness have been selected to represent the next generation. The frequencies of single letter have been used as an essential factor in the fitness function of the adopted GAs operations for affine cryptanalysis. By this tool, a high number of letters have been recovered to discover a plaintext of 375 letters by a fitness value of 95% at 120 generations in less than three minutes as compared to classical affine cryptanalysis without using GAs.

affine cipher, genetic algorithms (GAs), cryptanalysis, fitness value.