This paper examines the confluence of two lines of research that seek to improve the performance of evolutionary computation systems through management of information flow. The first is hybridization; the second is using small population effects. Hybridization consists of restarting evolutionary algorithms with copies of best-of-population individuals drawn from many populations. Small population effects occur when an evolutionary algorithmpsilas performance, either speed or probability of premature convergence, is improved by use of a very small population. This paper presents a structure for evolutionary computation called a blender which performs hybridization of many small populations. The blender algorithm is tested on the PORS and Tartarus tasks. Substantial and significant effects result from varying the size of the small populations used and from varying the frequency with which hybridization is performed. The major effect results from changing the frequency of hybridization; the impact of population size is more modest. The parameter settings which yield best performance of the blender algorithm are remarkably consistent across all seven sets of experiments performed. Blender performance is found to be superior to other algorithms for six cases of the PORS problem. For Tartarus, blender performs well, but not as well as the previous hybridization experiments that motivated its development.