What is the tradeoff between population size and the number of generations in genetic algorithms Genetic algorithms evolve in fewer generations with a larger population, but also take longer to compute a generation. Are there some guide lines for balancing those two factors, in order to arrive at a viable solution as soon as possible?

Also, is this the best place for the question?
There are some works on this issue. Since you don't specify for what problems you want guidelines I will list some major and some last papers of general interest. They are:


C. Witt (2008) Population Size versus Runtime of a Simple Evolutionary Algorithm // Theoretical Computer Science, 403(1), pp. 104-120.
T. Chen, K. Tang, G. Chen and X. Yao A large population size can be unhelpful in evolutionary algorithms // Theoretical Computer Science. Article in Press
O. Giel, P.K. Lehre (2010) On the effect of populations in evolutionary multi-objective optimisation // Evolutionary Computation 18 (3), pp. 335-356.
P.S. Oliveto, J. He, X. Yao (2008) Analysis of Population-based Evolutionary Algorithms for the Vertex Cover Problem. In Proceedings of the IEEE World Congress on Computational Intelligence.
P.S. Oliveto, J. He, X. Yao (2007) Time Complexity of Evolutionary Algorithms for Combinatorial Optimization: A Decade of Results // International Journal of Automation and Computing. See here 4.1 section for results on population-based EAs.