Probabilistic optimization of phenotype distributions: a general solution for the effects of uncertainty on natural selection?

Explorations of optimizing selection often find discrepancies between the theoretically expected and observed phenotypes. Such discrepancies are usually attributed to a variety of potential constraints. The authors suggest that one common constraint, environmental uncertainty, may reduce the applicability of traditional deterministic or stochastic optimization methods and that many apparent discrepancies might be artifacts of these methods. Since natural selection is essentially a statistical process, the authors propose that a probabilistic optimization procedure, that includes all of the variability in phenotype distributions and associated fitness potential functions might offer better results.