1. A process comprising: receiving at a computer processor a dynamic constraint on a genetic algorithm; applying dynamic programming to the genetic algorithm in the computer processor such that the genetic algorithm accommodates the dynamic constraint; and allocating load demand using the computer processor over a finite number of time intervals among a number of power generating units as a function of the constraint on the genetic algorithm and the dynamic programming of the genetic algorithm; wherein the receiving the dynamic constraint and the applying the dynamic programming to the genetic algorithm comprises modifying one or more initialization, crossover, and mutation operators, thereby maintaining load dispatch solutions evolving freely near a feasible region; and wherein the modifying of the mutation operator comprises heuristically maintaining locations of possible mutation points, and providing a positive mutation operator to reduce fuel cost and a negative mutation operator to achieve load equality.