Genetic Algorithms for Estimation Problems With Multiple Optima, Nondifferentiability, and Other Irregular Features

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Abstract
The genetic algorithm is examined as a method for solving optimization problems in econometric estimation. It does not restrict either the form or regularity of the objective function, allows a reasonably large parameter space, and does not rely on a point-to-point search. The performance is evaluated through two sets of experiments on standard test problems as well as econometric problems from the literature. First, alternative genetic algorithms that vary over mutation and crossover rates, population sizes, and other features are contrasted. Second, the genetic algorithm is compared to Nelder–Mead simplex, simulated annealing, adaptive random search, and MSCORE.