<![CDATA[The purpose of this research is to solve the Unit Commitment Problem (UCP), which is a critical task in power system optimization. The UCP involves determining the optimal scheduling of power generating units over a specified time horizon to meet the electricity demand while minimizing costs and satisfying operational constraints. In this study, a Genetic Algorithm (GA) method is proposed to solve the UCP efficiently. GA is inspired by the process of natural selection and evolution and is often used to solve complex optimization problems where traditional methods may be inefficient. The algorithm proceeds through several steps, namely parameters initialization, generating population, modification, calculating fitness function, parent selection, crossover, and mutation. The implementation of GA to solve UCP using C++ includes four different scenarios: a system with 4 units, 5 units, 10 units, and 26 units. The results obtained from the implementation of the GA on the different data sets indicate that the more iterations and the bigger initial population, the smaller the solution in the form of the total cost incurred.]]>
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