ain menu Main menu move to sidebar hide Navigation Main page Contents Current events Random article About Wikipedia Contact us Contribute Help Learn to edit Community portal Recent changes Upload file Special pages Search Search Appearance Donate Create account Log in Personal tools Donate Create account Log in Contents move to sidebar hide (Top) 1 History 2 See also 3 References 4 External links Toggle the table of contents Evolutionary programming 12 languages العربية Bosanski Deutsch Español فارسی Bahasa Indonesia Italiano 日本語 Македонски Polski Русский Українська Edit links Article Talk English Read Edit View history Tools Tools move to sidebar hide Actions Read Edit View history General What links here Related changes Upload file Permanent link Page information Cite this page Get shortened URL Print/export Download as PDF Printable version In other projects Wikidata item Appearance move to sidebar hide From Wikipedia, the free encyclopedia Evolutionary algorithm with a defined structure Part of a series on the Evolutionary algorithm Chromosome Fitness function Genetic operator Crossover Mutation Selection Population model Genetic algorithm (GA) Chromosome Clonal selection algorithm Fly algorithm Genetic fuzzy systems Genetic memory Schema Promoter based GA Genetic programming (GP) Cartesian GP Linear GP Gene expression programming Grammatical evolution Multi expression programming Differential evolution Evolution strategy Evolutionary programming Related topics Cellular EA Cultural algorithm Effective fitness Evolutionary computation Gaussian adaptation Grammar induction Evolutionary multimodal optimization Memetic algorithm Neuroevolution v t e 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 {\displaystyle \mu +\lambda } ) in one detail.[1] All individuals are selected for the new population, while in ES( μ + λ {\displaystyle \mu +\lambda } ), every individual has the same probability to be selected. It is one of the four major evolutionary algorithm paradigms.[3] History [edit] 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 [edit] Genetic algorithm Genetic operator References [edit] ^ a b c 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. ^ 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. ISBN 978-0-470-54460-0. ^ 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. ^ Fogel, LJ; Owens, AJ; Walsh, MJ (1966). rtificial intelligence thorough simulated evolution. New York: Wiley. ^ Xin Yao; Yong Liu; Guangming Lin (July 1999). "Evolutionary programming made faster". IEEE Transactions on Evolutionary Computation. 3 (2): 82–102. Bibcode:1999ITEC....3...82X. doi:10.1109/4235.771163. ^ 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. ^ 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. ^ 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. ^ 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. ^ 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. ^ 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. ^ 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. ^ 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. ^ 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 [edit] 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 v t e Evolutionary computation Main Topics Evolutionary algorithm Evolutionary data mining Evolutionary multimodal optimization Human-based evolutionary computation Interactive evolutionary computation Algorithms Cellular evolutionary algorithm Covariance Matrix Adaptation Evolution Strategy (CMA-ES) Cultural algorithm Differential evolution Evolutionary programming Genetic algorithm Genetic programming Gene expression programming Evolution strategy Natural evolution strategy Neuroevolution Learning classifier system Related techniques Swarm intelligence Ant colony optimization Bees algorithm Cuckoo search Particle swarm optimization Bacterial Colony Optimization Metaheuristic methods Firefly algorithm Harmony search Gaussian adaptation Memetic algorithm Related topics Artificial development Artificial intelligence Artificial life Digital organism Evolutionary robotics Fitness function Fitness landscape Fitness approximation Genetic operators Interactive evolutionary computation No free lunch in search and optimization Machine learning Mating pool Premature convergence Program synthesis Organizations ACM IEEE ACM SIGEVO IEEE CIS Conferences CEC GECCO PPSN EvoStar FOGA Journals Evolutionary Computation (journal) IEEE Trans Evol Comput ACM Trans Evol Learning Optim Authority control databases International GND National United States France BnF data Israel Other Yale LUX This computer science article is a stub. You can help Wikipedia by adding missing information. v t e Retrieved from "https://en.wikipedia.org/w/index.php?title=Evolutionary_programming&oldid=1335350172" Categories: Evolutionary algorithms Computer science stubs Hidden categories: Articles with short description Short description is different from Wikidata Webarchive template wayback links All stub articles This page was last edited on 28 January 2026, at 19:32 (UTC). Text is available under the Creative Commons Attribution-ShareAlike 4.0 License; additional terms may apply. By using this site, you agree to the Terms of Use and Privacy Policy. Wikipedia® is a registered trademark of the Wikimedia Foundation, Inc., a non-profit organization. Privacy policy About Wikipedia Disclaimers Contact Wikipedia Legal & safety contacts Code of Conduct Developers Statistics Cookie statement Mobile view Search Search Toggle the table of contents Evolutionary programming 12 languages Add topic