Genetic algorithms (GAs) represent a class of adaptive search techniqu
es based on a direct analogy, to Darwinian natural selection and mutat
ions in biological systems. ''Standard'' GAs have emphasized the utili
zation of binary codes. However, recent empirical, results have indica
ted that a chromosome representation which utilizes real values have e
nhanced the performance of these GAs in certain engineering problems.
A real-valued Generic Algorithm method described in this paper estimat
es the parameter values from an unconstrained population of data point
s for a Weibull distribution function using a simultaneous random sear
ch function by integrating the principles of the Generic Algorithm and
the method of Maximum Likelihood Estimation. The results of the real
coded GA technique for parameter estimation are compared to the result
s of the Newton-Raphson Algorithm.