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() in one detail. All individuals are selected for the new population, while in ES(), 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.
+ Timeline of EP - selected algorithms Year Description Reference 1966 EP introduced by Fogel et al. 1992 Improved fast EP - Cauchy mutation is used instead of Gaussian mutation 2002 Generalized EP - usage of Lévy-type mutation 2012 Diversity-guided EP - Mutation step size is guided by diversity 2013 Adaptive EP - The number of successful mutations determines the strategy parameter 2014 Social EP - Social cognitive model is applied meaning replacing individuals with cognitive agents 2015 Immunised EP - Artificial immune system inspired mutation and selection 2016 Mixed mutation strategy EP - Gaussian, Cauchy and Lévy mutations are used 2017 Fast Convergence EP - An algorithm, which boosts convergence speed and solution quality 2017 Immune log-normal EP - log-normal mutation combined with artificial immune system 2018 ADM-EP - automatically designed mutation operators
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)
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