This paper presents a new approach to the unit commitment problem using the absolutely stochastic simulated annealing method. In each iteration, a solution is taken with a certain probability. Typically, in the simulated annealing minimization method, a higher cost feasible solution is accepted with temperature-dependent probability, but other solutions are accepted deterministically. That may lead to the near optimization slowly. However in this paper, all the solutions, both higher and lower cost, are associated with acceptance probabilities. Besides, the number of bits flipping is decided by an appropriate distribution. Excess units with system-dependent probability distribution handle constraints efficiently. To reduce the economic load dispatch (ELD) overhead recalculations, a sign bit vector is introduced as well. The proposed method is tested using the reported problem data sets. Simulation results for the systems up to 100 units are compared to the previous reported results. Numerical results show an improvement in solution cost and time over to the results obtained from recent powerful algorithms.