GA Background:
Genetic algorithms (GA) have attracted significant research attention in many fields.   GA are proposed as solutions to difficult search problems. An interesting aspect of genetic search techniques is that they are equally effective in numerical and non-numerical search problems.  When formulated properly, GA can outperform purely random search methods.

This power, however, comes at a price. On numerical search problems that involve smooth, well behaved functions, GA methods are not as effective as traditional search methods that exploit traditional information such as the first and second derivatives. In these problems, GA methods can be wasteful and may not yield the desired level of accuracy.

This first step in effective use of these methods, therefore, is to assess whether they are appropriate for the problems the analysis is attempting to address. This is not an easy job and requires sepcial skills and experience in optimization problem solving - a requirement that are hard to meet by most practicing engineeers and field operators.

Other issues with GA:

(1) need to know ranges of variables before using a GA method
(2) speed is slow
(3) degree of optimization is questionable
(4) result has randomness