Evolutionary programming
Evolutionary programming is an evolutionary algorithm, where a share of new population is created by mutation of 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.| 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 |