Evolutionary programming
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Evolutionary programming is an evolutionary algorithm, where a share of the new population is created by mutating the previous population without crossover. Evolutionary programming differs from evolution strategy ES( \mu+\lambda ) in one detail. All individuals are selected for the new population, while in ES( \mu+\lambda ), every individual has the same probability to be selected. It is one of the four major evolutionary algorithm paradigms.
Evolutionary programming is an evolutionary algorithm, where a share of the new population is created by mutating the previous population without crossover. Evolutionary programming differs from evolution strategy ES( \mu+\lambda ) in one detail. All individuals are selected for the new population, while in ES( \mu+\lambda ), every individual has the same probability to be selected. It is one of the four major evolutionary algorithm paradigms. ==History== It was first used by Lawrence J. Fogel in the US in 1960 in order to use simulated evolution as a learning process aiming to generate artificial intelligence. It was used to evolve finite-state machines as predictors. ==See also== Genetic algorithm Genetic operator ==References== ==External links== The Hitch-Hiker's Guide to Evolutionary Computation: What's Evolutionary Programming (EP)? Evolutionary Programming by Jason Brownlee (PhD) de:Evolutionäre Programmierung
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