Fig. 3. (A) Outline of the steps that make up the microgenetic algorithm (µGA), starting from an initial population. We terminated the µGA after 50 generations and used a simplex search algorithm follow the gradient from the best µGA result to the local maxima. (B) How the combination of a µGA and simplex search might operate in a two-dimensional parameter space defined by the function z=f(x,y). The µGA searches broadly, improving slightly with every generation, while the simplex algorithm proceeds from the best µGA result to the local maximum. Note that although the example here shows a search for a maximum for ease of illustration, the moth simulation searches for a minimum using an otherwise identical procedure.