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