Cellular Genetic Algorithms(CGAs) are a subclass of Genetic Algorithms(GAs) in which each individuals are placed in a given geographical distribution. In general, CGAs# population space is a regular network that has relatively long characteristic path length and high clustering coefficient in the view of the Networks Theory. Long average path length makes the genetic interaction of remote nodes slow. If we have the population#s path length shorter with keeping the high clustering coefficient value, CGAs# population space will converge faster without loss of diversity. In this paper, we propose Smallest-Small-World Cellular Genetic Algorithms(SSWCGAs). In SSWCGAs, each individual lives in a population space that is highly clustered but having shorter characteristic path length, so that the SSWCGAs promote exploration of the search space with no loss of exploitation tendency that comes from being clustered. Some experiments along with four real variable functions and two GA-hard problems show that the SSWCGAs are more effective than SGAs and CGAs.