Abstract
This paper deals with an interpretation on stochastic combinatorial optimization algorithm using Markov process and Information theory. Combinatorial optimization problems are essential for seeking a specific set among other alternatives. These problems cannot be solved by standard optimization techniques such as the Newton method. And, these problems are almost always exposed to the danger of falling in local minima. Under these circumstances, techniques which integrate biological evolution process, physical process, and stochastic processing have been developed. These techniques are usually cast into a probabilistic and information framework via such paradigms as conbinatorial optimization, stochastic-based algorithm.
