Dynamic and Partially Connected Ring Topologies for Evolutionary Algorithms with Structured Populations

This paper investigates dynamic and partially connected ring topologies for cellular Evolutionary Algorithms (cEA). We hypothesize that these structures maintain population diversity at a higher level and reduce the risk of premature convergence to local optima on deceptive, multimodal and NP-hard fitness landscapes. A general framework for modelling partially connected topologies is proposed and three different schemes are tested. The results show that the structures improve the rate of convergence to global optima when compared to cEAs with standard topologies (ring, rectangular and square) on quasideceptive, deceptive and NP-hard problems. Optimal population size tests demonstrate that the proposed topologies require smaller populations when compared to traditional cEAs.