Genetic Algorithms
Genetic algorithms are robust search algorithms, that do not require knowledge of the objective function to be optimized and search through large spaces quickly. Genetic algorithms have been derived from the processes of the molecular biology of the gene and the evolution of life. Their operators, cross-over, mutation, and reproduction, are isomorphic with the synonymous biological processes. Genetic algorithms have been used to solve a variety of complex optimization problems. Additionally the classifier systems and the genetic programming paradigm have shown us that genetic algorithms can be used for tasks as complex as the program induction.
Read more about this topic: Quality Control And Genetic Algorithms
Famous quotes containing the word genetic:
“Man is not merely the sum of his masks. Behind the shifting face of personality is a hard nugget of self, a genetic gift.... The self is malleable but elastic, snapping back to its original shape like a rubber band. Mental illness is no myth, as some have claimed. It is a disturbance in our sense of possession of a stable inner self that survives its personae.”
—Camille Paglia (b. 1947)