A GENETIC ALGORITHM SOLUTION TO THE UNIT COMMITMENT PROBLEM

This paper presents a Genetic Algorithm (GA) solution to the Unit Comm itment problem. GAs are general purpose optimization techniques based on principles inspired from; the biological evolution using metaphors of mechanisms such as natural selection, genetic recombination and sur vival of the fittest. A simple GA algorithm implementation using the s tandard crossover and mutation operators could locate near optimal sol utions but in most cases failed to converge to the optimal solution. H owever, using the Varying Quality function technique and adding proble m specific operators, satisfactory solutions to the Unit Commitment pr oblem were obtained. Test results for systems of up to 100 units and c omparisons with results obtained using Lagrangian Relaxation and Dynam ic Programming are also reported.