Fault tolerant cellular Genetic Algorithm

This paper presents a cellular Genetic Algorithm (cGA) which aims at realizing a fault tolerant platform based on the inherent ability of cGAs to deal with Single Hard Errors (SHE) that could permanently affect the operation of a system. To attain this objective it is indispensable to control the parameters of the cGA which directly affect the efficiency and accuracy of its search process. Among the overall set of parameters, the migration rate and frequency, the grid size, and the shape and size of local neighbourhoods have a remarkable effect on the cGA performance. By appropriately controlling these parameters, the complex search space (presenting multi-peak fitness-function) associated with the practical case study of the investigation herein presented, is conveniently explored in terms of efficiency and efficacy. Initially, fitness score registers have been identified as critical for proper systempsilas operation. In case, SHEs occur at these registers, the algorithm will ignore possible good solutions and rapidly spread bad individuals. Experiments results show the faults effects regarding convergence time, search rate and results accuracy, as well as the cGA improvement on faulty scenarios when migration is applied following different selection and replacement criteria or increasing selection intensity through different local neighbourhoods configurations.