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:
“We cannot think of a legitimate argument why ... whites and blacks need be affected by the knowledge that an aggregate difference in measured intelligence is genetic instead of environmental.... Given a chance, each clan ... will encounter the world with confidence in its own worth and, most importantly, will be unconcerned about comparing its accomplishments line-by-line with those of any other clan. This is wise ethnocentricism.”
—Richard Herrnstein (1930–1994)