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Evolutionary Computation (EC) is the field of study devoted to problem solving using simulated evolution. In this paper, evolutionary operators are applied and a number of societies of hill-climbers (SoHCs), such as a genetic SoHC (GSoHC) and an evolutionary SoHC (ESoHC) are employed for solving randomly generated distributed asymmetric constraint satisfaction problems (DisACSPs). Further, we develop an Estimation of Distribution Algorithm SoHC (EDA-SoHC) variant using a uniform mutation operator. This variant produces offspring by drawing genetic material from a distribution of above-average individuals in the population. In this paper, we compare GSoHCs using distributed restricted forms of single-point, two-point, modified two-point, and uniform crossover. The GSoHCs are also compared with an ESoHC that uses a distributed restricted form of uniform mutation and a simple SoHC which does not use any evolutionary operators. Finally, we compare the SoHC, GSoHCs, and ESoHC to the EDA-SoHC.