Evoluciono računarstvo
Deo serije o evolucijskoj biologiji |
Evoluciona biologija |
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U računarskoj nauci, evoluciono računarstvo je porodica algoritama za globalnu optimizaciju inspirisana biološkom evolucijom,[1][2][3][4][5] i podoblast veštačke inteligencije i mekog računarstva koja proučava ove algoritme. U tehničkom smislu, oni su populaciono zasnovana porodica rešavanja problema tipa pokušaja i grešaka sa metaheurističkim[6] ili stohastičkim karakterom optimizacije.[7][8][9]
U evolucionom proračunu, početni skup rešenja kandidata se generiše i iterativno ažurira. Svaka nova generacija se proizvodi stohastičkim uklanjanjem manje željenih rešenja i uvođenjem malih nasumičnih promena kao i, u zavisnosti od metode, mešanjem roditeljskih informacija. U biološkoj terminologiji, populacija rešenja je podvrgnuta prirodnoj selekciji (ili veštačkoj selekciji), mutaciji i eventualno rekombinaciji. Kao rezultat, populacija će postepeno evoluirati kako bi se povećala fitnes, u ovom slučaju izabrane funkcije fitnesa algoritma.[10][11]
Evolucione tehnike računanja mogu da proizvedu visoko optimizovana rešenja u širokom spektru podešavanja problema, što ih čini popularnim u računarskoj nauci. Postoje mnoge varijante i proširenja, prilagođena specifičnijim porodicama problema i struktura podataka. Evoluciono računanje se takođe ponekad koristi u evolucionoj biologiji kao in silico eksperimentalna procedura za proučavanje uobičajenih aspekata opštih evolucionih procesa.
Reference
[уреди | уреди извор]- ^ Hall & Hallgrímsson 2008, стр. 4–6
- ^ „Evolution Resources”. Washington, DC: National Academies of Sciences, Engineering, and Medicine. 2016. Архивирано из оригинала 3. 6. 2016. г.
- ^ Scott-Phillips, Thomas C.; Laland, Kevin N.; Shuker, David M.; et al. (мај 2014). „The Niche Construction Perspective: A Critical Appraisal”. Evolution. 68 (5): 1231—1243. ISSN 0014-3820. PMC 4261998 . PMID 24325256. doi:10.1111/evo.12332. „Evolutionary processes are generally thought of as processes by which these changes occur. Four such processes are widely recognized: natural selection (in the broad sense, to include sexual selection), genetic drift, mutation, and migration (Fisher 1930; Haldane 1932). The latter two generate variation; the first two sort it.”
- ^ Hall & Hallgrímsson 2008, стр. 3–5
- ^ Voet, Voet & Pratt 2016, стр. 1–22, Chapter 1: Introduction to the Chemistry of Life
- ^ Sörensen, Kenneth (2015). „Metaheuristics—the metaphor exposed” (PDF). International Transactions in Operational Research. 22: 3—18. CiteSeerX 10.1.1.470.3422 . S2CID 14042315. doi:10.1111/itor.12001. Архивирано из оригинала (PDF) 2013-11-02. г.
- ^ Spall, J. C. (2003). Introduction to Stochastic Search and Optimization. Wiley. ISBN 978-0-471-33052-3.
- ^ Fu, M. C. (2002). „Optimization for Simulation: Theory vs. Practice”. INFORMS Journal on Computing. 14 (3): 192—227. doi:10.1287/ijoc.14.3.192.113.
- ^ M.C. Campi and S. Garatti. The Exact Feasibility of Randomized Solutions of Uncertain Convex Programs. SIAM J. on Optimization, 19, no.3: 1211–1230, 2008.[1]
- ^ Wassersug, J. D., and R. J. Wassersug, 1986. Fitness fallacies. Natural History 3:34–37.
- ^ Kimura, James F. Crow, Motoo (1970). An introduction to population genetics theory ([Reprint] изд.). New Jersey: Blackburn Press. стр. 5. ISBN 978-1-932846-12-6.
Literatura
[уреди | уреди извор]- Th. Bäck, D.B. Fogel, and Z. Michalewicz (Editors), Handbook of Evolutionary Computation, 1997, ISBN 0750303921
- Th. Bäck and H.-P. Schwefel. An overview of evolutionary algorithms for parameter optimization. Архивирано јул 12, 2018 на сајту Wayback Machine Evolutionary Computation. 1 (1): 1–23.
- W. Banzhaf, P. Nordin, R.E. Keller, and F.D. Francone. Genetic Programming — An Introduction. Morgan Kaufmann, 1998.
- S. Cagnoni, et al., Real-World Applications of Evolutionary Computing, Springer-Verlag Lecture Notes in Computer Science, Berlin, 2000.
- R. Chiong, Th. Weise, Z. Michalewicz (Editors), Variants of Evolutionary Algorithms for Real-World Applications, Springer, 2012, ISBN 3642234232
- K. A. De Jong, Evolutionary computation: a unified approach. MIT Press, Cambridge MA, 2006
- Eiben, Agoston E.; Smith, Jim (2015). „From evolutionary computation to the evolution of things”. Nature. 521 (7553): 476—482. Bibcode:2015Natur.521..476E. PMID 26017447. doi:10.1038/nature14544.
- A. E. Eiben and J.E. Smith, Introduction to Evolutionary Computing, Springer, First edition, 2003; Second edition, 2015
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- Z. Michalewicz and D.B. Fogel, How to Solve It: Modern Heuristics, Springer, 2004, ISBN 978-3-540-22494-5
- I. Rechenberg. Evolutionstrategie: Optimierung Technischer Systeme nach Prinzipien des Biologischen Evolution. Fromman-Hozlboog Verlag, Stuttgart, 1973. Шаблон:In lang
- H.-P. Schwefel. Numerical Optimization of Computer Models. John Wiley & Sons, New-York, 1981. 1995 – 2nd edition.
- D. Simon. Evolutionary Optimization Algorithms Архивирано март 10, 2014 на сајту Wayback Machine. Wiley, 2013.
- M. Sipper; W. Fu; K. Ahuja; J. H. Moore (2018). „Investigating the parameter space of evolutionary algorithms”. BioData Mining. 11: 2. PMC 5816380 . PMID 29467825. doi:10.1186/s13040-018-0164-x .
- Y. Zhang; S. Li. (2017). „PSA: A novel optimization algorithm based on survival rules of porcellio scaber”. arXiv:1709.09840 [cs.NE].