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] It was used to evolve finite-state machines as predictors.[3]
It is one of the four major evolutionary algorithm paradigms.[4]
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.[5]
See also
References
- ^ Slowik, Adam; Kwasnicka, Halina (1 August 2020). "Evolutionary algorithms and their applications to engineering problems". Neural Computing and Applications. 32 (16): 12363–12379. doi:10.1007/s00521-020-04832-8. ISSN 1433-3058.
- ^ Abido, Mohammad A.; Elazouni, Ashraf (30 November 2021). "Modified multi-objective evolutionary programming algorithm for solving project scheduling problems". Expert Systems with Applications. 183: 115338. doi:10.1016/j.eswa.2021.115338. ISSN 0957-4174.
- ^ Abraham, Ajith; Nedjah, Nadia; Mourelle, Luiza de Macedo (2006). "Evolutionary Computation: from Genetic Algorithms to Genetic Programming". Genetic Systems Programming: Theory and Experiences. Springer: 1–20. doi:10.1007/3-540-32498-4_1.
- ^ Brameier, Markus (2004). "On Linear Genetic Programming". Dissertation. Retrieved 27 December 2024.
- ^ "Artificial Intelligence through Simulated Evolution". Evolutionary Computation. 2009. doi:10.1109/9780470544600.ch7.
External links
- The Hitch-Hiker's Guide to Evolutionary Computation: What's Evolutionary Programming (EP)?
- Evolutionary Programming by Jason Brownlee (PhD) Archived 2013-01-18 at the Wayback Machine
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