This paper represents the principle of stochastic optimization and clarifies the rule of probability density function estimation in probabilistic optimization methods. Because of stochastic nature being in renewable energy power sources, during recent years, mentioned method has frequently and effectively been used and considered in several experimental sectors like renewable and hybrid power generation systems. Meanwhile, it describes and compares analytical, Monte Carlo simulation, point estimation and two-point estimation methods to estimate probability density functions. Several simulation results have been represented to clarify the offered concept. Finally, mentioned methods are implemented on a stochastic GA problem and the simulation results have been represented and compared.

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