Fault-tolerant network computation of individuals in genetic algorithms

Many genetic algorithms have complex fitness functions which can easily be calculated in parallel, given the tools to do so. This paper explores the use of a tool to gather spare computing cycles from a variable set of machines to allow convergence of GAs of this type. A modification to the steady-state model for GAs allows us to use the fault-prone behavior of an underlying thin networked computation system as noise within the GA itself. This "real" noise is incorporated into the GA, maintaining the drive towards convergence in the case of the heavily noisy network environment