We show how a genetic algorithm together with local energy minimisation can be used to find low energy configurations on the potential energy surface of disordered periodic systems. Examples include test cases (one and two dimensional periodic Ising models) alloys (Au-Cu) and mineral solid solution (CaCO₃-MgCO₃). We pay particular attention to challenges in maintaining the diversity in the population due to constraints associated with the fixed composition and synchronisation. We discuss briefly scaling with system size and the tuning of the parameters of the genetic algorithm. The genetic algorithm presented allows the study of disordered material in cases where a few configurations are thermally accessible and opens the way to efficient material design involving systems with a disordered structure.

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