List of genetic algorithm applications

This is a list of genetic algorithm (GA) applications.

• Airlines revenue management^[1]
• Artificial creativity
• Audio watermark insertion/detection
• Automated design = computer-automated design
• Automated design of mechatronic systems using bond graphs and genetic programming (NSF)
• Automated design of industrial equipment using catalogs of exemplar lever patterns
• Automated design of sophisticated trading systems in the financial sector
• Automated design, including research on composite material design and multi-objective design of automotive components for crashworthiness, weight savings, and other characteristics
• Bayesian inference links to particle methods in Bayesian statistics and hidden Markov chain models^[2][3]
• Bioinformatics Multiple Sequence Alignment^[4][5][6]
• Bioinformatics: RNA structure prediction^[7]
• Bioinformatics: Motif Discovery^[8]
• Biology and computational chemistry^[9][10]
• Building phylogenetic trees.^[11]
• Calculation of bound states and local-density approximations
• Chemical kinetics (gas and solid phases)
• Climatology: Modelling global temperature changes^[12]
• Climatology: Estimation of heat flux between the atmosphere and sea ice^[13]
• Clustering, using genetic algorithms to optimize a wide range of different fit-functions.^[14]
• Code-breaking, using the GA to search large solution spaces of ciphers for the one correct decryption.^[15]
• Computer architecture: using GA to find out weak links in approximate computing such as lookahead.
• Computer-automated design ^[16]
• Configuration applications, particularly physics applications of optimal molecule configurations for particular systems like C₆₀ (buckyballs)
• Construction of facial composites of suspects by eyewitnesses in forensic science.^[17]
• Container loading optimization
• Control engineering,^{[18][19][20][21]}
• Data Center/Server Farm.^[22]
• Design of water resource systems ^[23]
• Design of anti-terrorism systems ^[24]
• Distributed computer network topologies
• Electronic circuit design, known as evolvable hardware
• Gene expression profiling analysis.^[25]
• Feynman-Kac models ^[26][27][28]
• Financial mathematics^[3][29]
• File allocation for a distributed system
• Filtering and signal processing ^[30][31]
• Finding hardware bugs.^[32][33]
• Game theory equilibrium resolution
• Genetic Algorithm for Rule Set Production
• Economics
• Scheduling applications, including job-shop scheduling and scheduling in printed circuit board assembly.^[34] The objective being to schedule jobs in a sequence-dependent or non-sequence-dependent setup environment in order to maximize the volume of production while minimizing penalties such as tardiness. Satellite communication scheduling for the NASA Deep Space Network was shown to benefit from genetic algorithms.^[35]
• Groundwater monitoring networks^[36]
• Learning robot behavior using genetic algorithms
• Image processing: Dense pixel matching^[37]
• Learning fuzzy rule base using genetic algorithms
• Linguistic analysis, including grammar induction and other aspects of Natural language processing (NLP) such as word sense disambiguation.
• Marketing mix analysis
• Mechanical engineering^[38][39]
• Medicine: Clinical decision support in ophthalmology^[40] and oncology^[41]
• Mobile communications infrastructure optimization.
• Molecular structure optimization (chemistry)
• Multidimensional systems
• Multimodal Optimization ^[42][43][44]
• Multiple criteria production scheduling^[45]
• Multiple population topologies and interchange methodologies
• Mutation testing
• Neural Networks; particularly recurrent neural networks^[46]
• Operon prediction.^[47]
• Optimisation of data compression systems, for example using wavelets.
• Parallelization of GAs/GPs including use of hierarchical decomposition of problem domains and design spaces nesting of irregular shapes using feature matching and GAs.
• Plant floor layout
• Pop music record production^[48]
• Power electronics design.^[49]
• Protein folding and protein/ligand docking^[50][51]
• Quality control
• Rare event analysis ^[52][53]
• Real options valuation ^[54]
• Representing rational agents in economic models such as the cobweb model
• Selection of optimal mathematical model to describe biological systems
• Software engineering
• Solving the machine-component grouping problem required for cellular manufacturing systems
• Stochastic optimization ^[55]
• Tactical asset allocation and international equity strategies
• Timetabling problems, such as designing a non-conflicting class timetable for a large university
• Training artificial neural networks when pre-classified training examples are not readily obtainable (neuroevolution)
• Traveling salesman problem and its applications^[34]
• Vehicle routing problems with multiple soft time windows, multiple depots and an heterogeneous fleet
• Wireless sensor/ad-hoc networks.^[56]

References

1. Aloysius George, B. R. Rajakumar, D. Binu,(2012) "Genetic algorithm based airlines booking terminal open/close decision system"
2. "Del Moral - Bayesian Statistics". u-bordeaux1.fr.
3. a tutorial on genetic particle models
4. Gondro C, Kinghorn BP (2007). "A simple genetic algorithm for multiple sequence alignment". Genetics and Molecular Research 6 (4): 964–982. PMID 18058716.
5. Notredame C, Higgins DG (1995). "SAGA a Genetic Algorithm for Multiple Sequence Alignment". Nucleic Acids Research 24 (8): 1515–24. doi:10.1093/nar/24.8.1515. PMC 145823. PMID 8628686.
6. "Notredame Lab Home Page - Comparative Bioinformatics". tcoffee.org.
7. van Batenburg FH, Gultyaev AP, Pleij CW (1995). "An APL-programmed genetic algorithm for the prediction of RNA secondary structure". Journal of Theoretical Biology 174 (3): 269–280. doi:10.1006/jtbi.1995.0098. PMID 7545258.
8. "Generalizing and learning protein-DNA binding sequence representations by an evolutionary algorithm". springerlink.com.
9. "Del Moral - Biology & Chemistry". u-bordeaux1.fr.
10. an article on genetic particle models
11. Hill T, Lundgren A, Fredriksson R, Schiöth HB (2005). "Genetic algorithm for large-scale maximum parsimony phylogenetic analysis of proteins". Biochimica et Biophysica Acta 1725 (1): 19–29. doi:10.1016/j.bbagen.2005.04.027. PMID 15990235.
12. Karolina Stanislawska, Krzysztof Krawiec, Zbigniew W. Kundzewicz (April 2012). "Modelling global temperature changes with genetic programming.". Computers and Mathematics with Applications.
13. Karolina Stanislawska, Krzysztof Krawiec, Timo Vihma (July 15, 2015). "Genetic Programming for Estimation of Heat Flux between the Atmosphere and Sea Ice in Polar Regions.".
14. Auffarth, B. (2010). Clustering by a Genetic Algorithm with Biased Mutation Operator. WCCI CEC. IEEE, July 18–23, 2010. http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.170.869
15. Joachim De Zutter
16. Li, Y., et al. (2004). CAutoCSD – Evolutionary search and optimisation enabled computer automated control system design. International Journal of Automation and Computing, 1(1). pp. 76–88. ISSN 1751-8520, http://eprints.gla.ac.uk/3818/
17. Craig Aaen Stockdale (June 1, 2008). "A (r)evolution in Crime-fighting.". Forensic Magazine.
18. "CiteSeerX — Citation Query Switching Control Systems and Their Design Automation via Genetic Algorithms". psu.edu.
19. Li, Y.; et al. (1996). "Genetic algorithm automated approach to design of sliding mode control systems". Int J Control 63 (4): 721–739. doi:10.1080/00207179608921865. CiteSeerX: 10.1.1.43.1654.
20. "Loughborough University Institutional Repository". handle.net.
21. Patrascu, M. (2015). "Genetically enhanced modal controller design for seismic vibration in nonlinear multi-damper configuration". Proc.I.Mech. Part I: Journal of Systems and Control Engineering 229 (2): 158–168. doi:10.1177/0959651814550540.
22. SymbioticSphere – Distributed Software Systems Group, University of Massachusetts, Boston
23. Zhang, S.X.; Babovic, V. (2012). "A real options approach to the design and architecture of water supply systems using innovative water technologies under uncertainty". Journal of Hydroinformatics 14 (1): 13–29. doi:10.2166/hydro.2011.078.
24. Buurman, J.; Zhang, S.X.; Babovic, V. (2009). "Reducing risk through real options in systems design: the case of architecting a maritime domain protection system". Risk Analysis 29 (3): 366–379. doi:10.1111/j.1539-6924.2008.01160.x.
25. To CC, Vohradsky J (2007). "A parallel genetic algorithm for single class pattern classification and its application for gene expression profiling in Streptomyces coelicolor". BMC Genomics 8: 49. doi:10.1186/1471-2164-8-49. PMC 1804277. PMID 17298664.
26. "Website for Feynman-Kac particle models". u-bordeaux1.fr.
27. a review article on genetic particle models
28. "Feynman-Kac Formulae". u-bordeaux1.fr.
29. "Del Moral - Financial Mathematics". u-bordeaux1.fr.
30. links to particle filters
31. a tutorial on genetic particle models
32. Hitoshi Iba, Sumitaka Akiba, Tetsuya Higuchi, Taisuke Sato: BUGS: A Bug-Based Search Strategy using Genetic Algorithms. PPSN 1992:
33. Ibrahim, W. and Amer, H.: An Adaptive Genetic Algorithm for VLSI Test Vector Selection
34. Maimon, Oded; Braha, Dan (1998). "A genetic algorithm approach to scheduling PCBs on a single machine" (PDF). International Journal of Production Research 36: 3.
35. Guillaume, A. (2007). "Deep space network scheduling using evolutionary computational methods". Aerospace Conference, 2007 IEEE: 1–6.
36. Optimization of Water-level Monitoring Networks in the Eastern Snake River Plain Aquifer Using a Kriging-based Genetic Algorithm Method United States Geological Survey
37. A. dos Santos-Paulino, J.-C. Nebel and F.Florez-Revuelta (2014) Evolutionary algorithm for dense pixel matching in presence of distortions, EvoStar Conference, Granada, Spain, 23–25 April 2014
38. "Genetic Algorithms for Engineering Optimization" (PDF).
39. "Applications of evolutionary algorithms in mechanical engineering.".
40. Krzysztof Krawiec, Mikołaj Pawlak (April 10, 2015). "Genetic Programming with Alternative Search Drivers for Detection of Retinal Blood Vessels".
41. Fitzgerald, Jeannie , Ryan, Conor , Medernach, David and Krawiec, Krzysztof (July 15, 2015). "An Integrated Approach to Stage 1 Breast Cancer Detection.".
42. "Effect of Spatial Locality on an Evolutionary Algorithm for Multimodal Optimization". springerlink.com.
43. "An evolutionary algorithm with species-specific explosion for multimodal optimization". acm.org.
44. "Evolutionary multimodal optimization using the principle of locality". sciencedirect.com.
45. Bagchi Tapan P (1999). "Multiobjective Scheduling by Genetic Algorithms". Multiobjective Scheduling by Genetic Algorithms (Kluwer Academic). ISBN 0-7923-8561-6.
46. "Applying Genetic Algorithms to Recurrent Neural Networks for Learning Network Parameters and Architecture". arimaa.com.
47. Wang S, Wang Y, Du W, Sun F, Wang X, Zhou C, Liang Y (2007). "A multi-approaches-guided genetic algorithm with application to operon prediction". Artificial Intelligence in Medicine 41 (2): 151–159. doi:10.1016/j.artmed.2007.07.010. PMID 17869072.
48. "To the beat of the byte". BBC News. 1998-07-01. Retrieved 2010-05-03.
49. Zhang, J., Lo, W.L., and Chung, H., "Pseudocoevolutionary Genetic Algorithms for Power Electronic Circuits Optimization", IEEE Trans Systems, Man, and Cybernetics, Part C., Vol.36, No.4, July 2006, pp. 590–598.
50. Willett P (1995). "Genetic algorithms in molecular recognition and design". Trends in Biotechnology 13 (12): 516–521. doi:10.1016/S0167-7799(00)89015-0. PMID 8595137.
51. "Protein structure prediction on a lattice model via multimodal optimization techniques". acm.org.
52. "Del Moral - Rare events". u-bordeaux1.fr.
53. a review article
54. Zhang, S.X.; Babovic, V. (2011). "An evolutionary real options framework for the design and management of projects and systems with complex real options and exercising conditions". Decision Support Systems 51 (1): 119–129. doi:10.1016/j.dss.2010.12.001.
55. "Del Moral - Optimal Control". u-bordeaux1.fr.
56. BiSNET/e – Distributed Software Systems Group, University of Massachusetts, Boston