Abstract
Genetic algorithms (GAs) represent a class of adaptive search techniques based on a direct analogy to Darwinian natural selection and mutations in biological systems. “Standard” GAs have emphasized the utilization of binary codes. However, recent empirical results have indicated that a chromosome representation which utilizes real values have enhanced the performance of these GAs in certain engineering problems. A real-valued Genetic Algorithm method described in this paper estimates the parameter values from an unconstrained population of data points for a Weibull distribution function using a simultaneous random search function by integrating the principles of the Genetic Algorithm and the method of Maximum Likelihood Estimation. The results of the real-coded GA technique for parameter estimation are compared to the results of the Newton-Raphson Algorithm.
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