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(<math>\mu+\lambda</math>) in one detail.
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.
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|+ Timeline of EP - selected algorithms
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| 1992 || Improved fast EP - Cauchy mutation is used instead of Gaussian mutation ||
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| 2002 || Generalized EP - usage of Lévy-type mutation ||
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| 2012 || Diversity-guided EP - Mutation step size is guided by diversity ||
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| 2013 || Adaptive EP - The number of successful mutations determines the strategy parameter ||
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| 2014 || Social EP - Social cognitive model is applied meaning replacing individuals with cognitive agents ||
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| 2015 || Immunised EP - Artificial immune system inspired mutation and selection ||
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| 2016 || Mixed mutation strategy EP - Gaussian, Cauchy and Lévy mutations are used ||
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| 2017 || Fast Convergence EP - An algorithm, which boosts convergence speed and solution quality ||
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| 2017 || Immune log-normal EP - log-normal mutation combined with artificial immune system ||
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| 2018 || ADM-EP - automatically designed mutation operators ||
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See also
- Genetic algorithm
- Genetic operator