Evolutionary programming

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Short description: Evolutionary algorithm with a defined structure
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Evolutionary algorithm
Genetic algorithm
Genetic 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(μ+λ) in one detail.[1] All individuals are selected for the new population, while in ES(μ+λ), 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

References

  1. ↑ 1.0 1.1 1.2 Slowik, Adam; Kwasnicka, Halina (1 August 2020). "Evolutionary algorithms and their applications to engineering problems" (in en). 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. doi:10.1016/j.eswa.2021.115338. ISSN 0957-4174. https://www.sciencedirect.com/science/article/abs/pii/S0957417421007673. 
  3. ↑ Brameier, Markus (2004). "On Linear Genetic Programming". Dissertation. http://d-nb.info:80/1011533146/34. 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" (in en). Genetic Systems Programming: Theory and Experiences. Studies in Computational Intelligence (Springer) 13: 1–20. doi:10.1007/3-540-32498-4_1. ISBN 978-3-540-29849-6. https://link.springer.com/chapter/10.1007/3-540-32498-4_1. 
  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. doi:10.1016/S0010-4655(02)00386-7. ISSN 0010-4655. Bibcode: 2002CoPhC.147..729I. https://www.sciencedirect.com/science/article/abs/pii/S0010465502003867. 
  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. https://www.sciencedirect.com/science/article/abs/pii/S1568494612000567. 
  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. https://www.sciencedirect.com/science/article/abs/pii/S221065021200079X. 
  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. https://www.sciencedirect.com/science/article/pii/S1474667016421658. 
  12. ↑ Gao, Wei (1 August 2015). "Slope stability analysis based on immunised evolutionary programming" (in en). Environmental Earth Sciences 74 (4): 3357–3369. doi:10.1007/s12665-015-4372-0. ISSN 1866-6299. Bibcode: 2015EES....74.3357G. https://link.springer.com/article/10.1007/s12665-015-4372-0. 
  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. https://www.sciencedirect.com/science/article/abs/pii/S1568494617306051. 

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