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This paper proposes and evaluates a method that improves the adaptability and efficiency of genetic algorithms (GAs) when applied to the minimal loss reconfiguration problem. This research reduces the searching space (population) when a new codification strategy and novel genetic operators, called accentuated crossover and directed mutation, are used. This allows a drastic reduction of the computational time and minimizes the memory requirements, ensuring a efficiency search when compared to current GA reconfiguration techniques. The reduced population is created through the branches that form "system loops." This means that almost all individuals created for the GA are feasible (radial networks) generating topologies that can only be limited by the system's operational constraints. The results of the proposed reconfiguration method are compared with other techniques, yielding smaller or equal power loss values with less computational efforts.