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.
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Famous quotes containing the word genetic:
“Nature, we are starting to realize, is every bit as important as nurture. Genetic influences, brain chemistry, and neurological development contribute strongly to who we are as children and what we become as adults. For example, tendencies to excessive worrying or timidity, leadership qualities, risk taking, obedience to authority, all appear to have a constitutional aspect.”
—Stanley Turecki (20th century)