A Comparison of Evolutionary Protocols for Solving Distributed Constraint Satisfaction Problems

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.