Genetic Programming
In artificial intelligence, genetic programming (GP) is an evolutionary algorithm-based methodology inspired by biological evolution to find computer programs that perform a user-defined task. It is a specialization of genetic algorithms (GA) where each individual is a computer program. It is a machine learning technique used to optimize a population of computer programs according to a fitness landscape determined by a program's ability to perform a given computational task.
Read more about Genetic Programming: History, Program Representation, Other Approaches, MOSES, Meta-Genetic Programming, Implementations
Famous quotes containing the words genetic and/or programming:
“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)
“If there is a price to pay for the privilege of spending the early years of child rearing in the driver’s seat, it is our reluctance, our inability, to tolerate being demoted to the backseat. Spurred by our success in programming our children during the preschool years, we may find it difficult to forgo in later states the level of control that once afforded us so much satisfaction.”
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