SAS PROC GA: Genetic algorithms are a family of local search algorithms that seek optimal solutions to problems by applying the principles of natural selection and evolution. Genetic algorithms can be applied to almost any optimization problem and are especially useful for problems where other calculus-based techniques do not work, such as when the objective function has many local optima, when it is not differentiable or continuous, or when solution elements are constrained to be integers or sequences. In most cases genetic algorithms require more computation than specialized techniques that take advantage of specific problem structures or characteristics. However, for optimization problems with no such techniques available, genetic algorithms provide a robust general method of solution
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References in zbMATH (referenced in 1 article )
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- Francisco Juretig: MLGA: A SAS Macro to Compute Maximum Likelihood Estimators via Genetic Algorithms (2015) not zbMATH