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GAs are general purpose optimization techniques based on principles inspired from the biological evolution using metaphors of mechanisms, such as natural selection, genetic re-combination and survival of the fittest. By use of coding better-ment, the dynamic changes of the mutation rate and the crossover probability, the dynamic choice of subsistence, the reservation of the optimal fitness value, a modified genetic algorithm for opti-mizing combination of units in thermal power plants is proposed. And through taking examples, test result are analyzed and com-pared with results of some different algorithms. Numerical results show available value for the unit commitment problem with ex-amples.
GAs are general purpose optimization techniques based on principles inspired from the biological evolution using metaphors of mechanisms, such as natural selection, genetic re-combination and survival of the fittest. By use of coding better-ment, the dynamic changes of the mutation rate and the crossover probability, the dynamic choice of subsistence, the reservation of the optimal fitness value, a modified genetic algorithm for opti-mizing combination of units in thermal power plants is proposed. And through taking examples, test result are analyzed and com-pared with results of some different algorithms. Numerical results show available value for the unit commitment problem with ex-amples.