Evolutionary programming

Evolutionary algorithm with a defined structure From Wikipedia, the free encyclopedia

Evolutionary programming

Evolutionary programming is an evolutionary algorithm, where a share of new population is created by mutation of previous population without crossover.[1][2] Evolutionary programming differs from evolution strategy ES() in one detail.[1] 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.[3]

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.[4] It was used to evolve finite-state machines as predictors.[5]

More information Year, Description ...
Timeline of EP - selected algorithms[1]
YearDescriptionReference
1966EP introduced by Fogel et al.[6]
1992Improved fast EP - Cauchy mutation is used instead of Gaussian mutation[7]
2002Generalized EP - usage of Lévy-type mutation[8]
2012Diversity-guided EP - Mutation step size is guided by diversity[9]
2013Adaptive EP - The number of successful mutations determines the strategy parameter[10]
2014Social EP - Social cognitive model is applied meaning replacing individuals with cognitive agents[11]
2015Immunised EP - Artificial immune system inspired mutation and selection[12]
2016Mixed mutation strategy EP - Gaussian, Cauchy and Lévy mutations are used[13]
2017Fast Convergence EP - An algorithm, which boosts convergence speed and solution quality[14]
2017Immune log-normal EP - log-normal mutation combined with artificial immune system[15]
2018ADM-EP - automatically designed mutation operators[16]
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See also

References

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