  The cryptanalysis of various cipher problems can be formulated as NP-Hard
combinatorial problem. Solving such problems requires time and/or memory
requirement which increases with the size of the problem. Techniques for
solving combinatorial problems fall into two broad groups - exact algorithms
and Evolutionary Computation algorithms. An exact algorithms guarantees that
the optimal solution to the problem will be found. The exact algorithms like
branch and bound, simplex method, brute force etc methodology is very
inefficient for solving combinatorial problem because of their prohibitive
complexity (time and memory requirement). The Evolutionary Computation
algorithms are employed in an attempt to find an adequate solution to the
problem. A Evolutionary Computation algorithm - Genetic algorithm, simulated
annealing and tabu search were developed to provide a robust and efficient
methodology for cryptanalysis. The aim of these techniques to find sufficient
"good" solution efficiently with the characteristics of the problem, instead of
the global optimum solution, and thus it also provides attractive alternative
for the large scale applications. This paper focuses on the methodology of
Evolutionary Computation algorithms .
