Performance of static random topologies in fine-grained QEA on P-PEAKS problem instances

Quantum inspired Evolutionary Algorithms (QEA) are a type of population based meta-heuristics, which have been successful in solving difficult search and optimization problems. It provides better balance between exploration and exploitation, when compared with conventional evolutionary algorithms as it is designed by integrating principles from quantum mechanics into the framework of evolutionary algorithms. Recently, a study was performed to find the effect of different population models on performance of QEA and it was found that performance of fine-grained population model was better than the other models. This paper investigates the effect of static random topologies on the performance of Quantum inspired evolutionary algorithm with fine-grained population model.