Cooperative coevolutionary algorithms (CCEA) are a form of evolutionary algorithm that is applicable when the problem can be decomposed into components. Each component is assigned a subpopulation that evolves a good solution to the subproblem. To compute an individual's fitness, it is combined with collaborators drawn from the other subpopulations to form a complete solution. The individual's fitness is a function of this solution's fitness. The contributors to the comprehensive fitness formula are known as collaborators. The number of collaborators allowed from each subpopulation is called pool size. It has been shown that the outcome of the CCEA can be improved by allowing multiple collaborators from each subpopulation. This results in larger pool sizes, but improved fitness. The improvement in fitness afforded by larger pool sizes is offset by increased calculation costs. This study targeted the pool size parameter of CCEAs by devising dynamic strategies for the assignment of pool size to regulate selection pressure. Subpopulations were rewarded with a larger pool size or penalized with a smaller pool size based on measures of their diversity and/or fitness. Measures for population diversity and fitness used in this study were derived from various works involving evolutionary computation. This study showed that dynamically assigning pool size based on these measures of the diversity and fitness of the subpopulations can yield improved fitness results with significant reduction in calculation costs over statically assigned pool sizes.