Evolutionary algorithms (EAs) are based on the underlying genetic process in biological organisms and on the natural evolution populations. Recently, EAs have found many potential applications in economical operation and control of power systems. However, when applying EAs to large scale system optimization problems, there is still a preeminent problem, the premature convergence problem, which degrade the EAs' performance. This paper presents an overview of the recent important techniques emerging in EAs' community with potential applications in power systems domains. These techniques covers both practical and theoretical considerations in self-adaptation and handling constraints. It is desired that they are helpful to the construction of some more efficient, robust EAs to solve a broader range of problems types in power systems.
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