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(${\displaystyle \mu +\lambda }$) in one detail.^[1] All individuals are selected for the new population, while in ES(${\displaystyle \mu +\lambda }$), 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]

Timeline of EP - selected algorithms^[1]

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

• Artificial intelligence
• Genetic algorithm
• Genetic operator

References

1. 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.
2. 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.
3. Brameier, Markus (2004). "On Linear Genetic Programming". Dissertation. Retrieved 27 December 2024.
4. "Artificial Intelligence through Simulated Evolution". Evolutionary Computation. 2009. doi:10.1109/9780470544600.ch7. ISBN 978-0-470-54460-0.
5. Abraham, Ajith; Nedjah, Nadia; Mourelle, Luiza de Macedo (2006). "Evolutionary Computation: from Genetic Algorithms to Genetic Programming". Genetic Systems Programming: Theory and Experiences. Studies in Computational Intelligence. 13. Springer: 1–20. doi:10.1007/3-540-32498-4_1. ISBN 978-3-540-29849-6.
6. Fogel, LJ; Owens, AJ; Walsh, MJ (1966). rtificial intelligence thorough simulated evolution. New York: Wiley.
7. Xin Yao; Yong Liu; Guangming Lin (July 1999). "Evolutionary programming made faster". IEEE Transactions on Evolutionary Computation. 3 (2): 82–102. doi:10.1109/4235.771163.
8. Iwamatsu, Masao (1 August 2002). "Generalized evolutionary programming with Lévy-type mutation". Computer Physics Communications. 147 (1): 729–732. Bibcode:2002CoPhC.147..729I. doi:10.1016/S0010-4655(02)00386-7. ISSN 0010-4655.
9. Alam, Mohammad Shafiul; Islam, Md. Monirul; Yao, Xin; Murase, Kazuyuki (1 June 2012). "Diversity Guided Evolutionary Programming: A novel approach for continuous optimization". Applied Soft Computing. 12 (6): 1693–1707. doi:10.1016/j.asoc.2012.02.002. ISSN 1568-4946.
10. Das, Swagatam; Mallipeddi, Rammohan; Maity, Dipankar (1 April 2013). "Adaptive evolutionary programming with p-best mutation strategy". Swarm and Evolutionary Computation. 9: 58–68. doi:10.1016/j.swevo.2012.11.002. ISSN 2210-6502.
11. Nan, LI; Xiaomin, BAI; Shouzhen, ZHU; Jinghong, ZHENG (1 January 2014). "Social Evolutionary Programming Algorithm onUnit Commitment in Wind Power Integrated System". IFAC Proceedings Volumes. 47 (3): 3611–3616. doi:10.3182/20140824-6-ZA-1003.00384. ISSN 1474-6670.
12. Gao, Wei (1 August 2015). "Slope stability analysis based on immunised evolutionary programming". Environmental Earth Sciences. 74 (4): 3357–3369. Bibcode:2015EES....74.3357G. doi:10.1007/s12665-015-4372-0. ISSN 1866-6299.
13. Pang, Jinwei; Dong, Hongbin; He, Jun; Feng, Qi (July 2016). "Mixed mutation strategy evolutionary programming based on Shapley value". 2016 IEEE Congress on Evolutionary Computation (CEC). pp. 2805–2812. doi:10.1109/CEC.2016.7744143. ISBN 978-1-5090-0623-6.
14. Basu, Mousumi (14 September 2017). "Fast Convergence Evolutionary Programming for Multi-area Economic Dispatch". Electric Power Components and Systems. 45 (15): 1629–1637. doi:10.1080/15325008.2017.1376234. ISSN 1532-5008.
15. Mansor, M.H.; Musirin, I.; Othman, M.M. (April 2017). "Immune Log-Normal Evolutionary Programming (ILNEP) for solving economic dispatch problem with prohibited operating zones". 2017 4th International Conference on Industrial Engineering and Applications (ICIEA). pp. 163–167. doi:10.1109/IEA.2017.7939199. ISBN 978-1-5090-6774-9.
16. Hong, Libin; Drake, John H.; Woodward, John R.; Özcan, Ender (1 January 2018). "A hyper-heuristic approach to automated generation of mutation operators for evolutionary programming". Applied Soft Computing. 62: 162–175. doi:10.1016/j.asoc.2017.10.002. ISSN 1568-4946.

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