List of genetic algorithm applications

This is a list of Genetic Algorithm (GA) applications

Applications of genetic algorithms

• Artificial creativity
• Audio watermark 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 ([1] links to particle methods in Bayesian statistics and hidden Markov chain models and [2] a tutorial on genetic particle models)
• Bioinformatics multiple sequence alignment.^[1]
• Bioinformatics: RNA structure prediction.^[2]
• Bioinformatics: Multiple Sequence Alignment.^[3] SAGA is available on:.^[4]
• Bioinformatics: Motif Discovery.^[5]
• Biology and computational chemistry ([3] links to particle methods in biology and computational chemistry and [4] an article on genetic particle models)
• Building phylogenetic trees.^[6]
• Calculation of bound states and local-density approximations.
• Chemical kinetics (gas and solid phases)
• Clustering. Using genetic algorithms to optimize a wide range of different fit-functions.^[7]
• Code-breaking, using the GA to search large solution spaces of ciphers for the one correct decryption.^[8]
• Computer-automated design ^[9]
• 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.^[10]
• Container loading optimization.
• Control engineering,.^[11][12][13]
• Data Center/Server Farm.^[14]
• Design of water distribution systems.
• Distributed computer network topologies.
• Electronic circuit design, known as evolvable hardware.
• Gene expression profiling analysis.^[15]
• Feynman-Kac models ([5] links to genetic type particle interpretations, [6] a review article on genetic particle models, and a research monograph [7])
• Financial Mathematics ([8] links to particle methods in mathematical finance and [9] a tutorial on genetic particle models)
• File allocation for a distributed system.
• Filtering and signal processing ([10] links to particle filters and [11] a tutorial on genetic particle models)
• Finding hardware bugs.^[16][17]
• Game theory equilibrium resolution.
• Genetic Algorithm for Rule Set Production
• Economics
• Scheduling applications, including job-shop scheduling. 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.
• Learning robot behavior using genetic algorithms.
• 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^[18][19]
• Mobile communications infrastructure optimization.
• Molecular structure optimization (chemistry).
• Multidimensional systems
• Multimodal Optimization ^[20][21][22]
• Multiple criteria production scheduling.^[23]
• Multiple population topologies and interchange methodologies.
• Mutation testing
• Neural Networks; particularly recurrent neural networks^[24]
• Operon prediction.^[25]
• 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 producer.^[26]
• Power electronics design.^[27]
• Protein folding and protein/ligand docking.^[28][29]
• Quality control
• Rare event analysis ([12] links to particle rare event simulation and [13] a review article)
• Representing rational agents in economic models such as the cobweb model.
• Selection of optimal mathematical model to describe biological systems.
• Software engineering ^{[citation needed]}
• Solving the machine-component grouping problem required for cellular manufacturing systems.
• Stochastic optimization ([14] links to particle methods in regulation, optimization, and optimal control)
• 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.
• Wireless sensor/ad-hoc networks.^[30]
• Airlines Revenue Management.<ref>[15]

References

1. Gondro C, Kinghorn BP (2007). "A simple genetic algorithm for multiple sequence alignment". Genetics and Molecular Research. 6 (4): 964–982. PMID 18058716.
2. 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.
3. Notredame C, Higgins DG (1995). "SAGA a Genetic Algorithm for Multiple Sequence Alignment". Nucleic Acids Research. 24 (8): 1515. doi:10.1093/nar/24.8.1515. PMC 145823. PMID 8628686.
4. Cedric Notredame Home Page
5. Ka-Chun Wong*, Chengbin Peng, Man-Hon Wong, Kwong-Sak Leung: Generalizing and learning protein-DNA binding sequence representations by an evolutionary algorithm. Soft Computing, 15:1631-1642, 2011.
6. 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.
7. 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
8. Joachim De Zutter
9. 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/
10. Craig Aaen Stockdale (June 1, 2008). "A (r)evolution in Crime-fighting". Forensic Magazine.
11. Ng, K.C., Switching Control Systems and Their Design Automation via Genetic Algorithms, PhD Thesis, University of Glasgow, 1995.
12. Li, Y. (1996). "Genetic algorithm automated approach to design of sliding mode control systems". Int J Control. pp. 721–739. CiteSeer^x: 10.1.1.43.1654 . {{cite web}}: Missing or empty |url= (help); Unknown parameter |coauthors= ignored (|author= suggested) (help)
13. K. Michail, Optimised Configuration of Sensing Elements for Control and Fault Tolerance Applied to an Electro-Magnetic Suspension, PhD Thesis, Loughborough University, UK, 2009.
14. SymbioticSphere – Distributed Software Systems Group, University of Massachusetts, Boston
15. 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.
16. Hitoshi Iba, Sumitaka Akiba, Tetsuya Higuchi, Taisuke Sato: BUGS: A Bug-Based Search Strategy using Genetic Algorithms. PPSN 1992:
17. Ibrahim, W. and Amer, H.: An Adaptive Genetic Algorithm for VLSI Test Vector Selection
18. "Genetic Algorithms for Engineering Optimization" (PDF).
19. "Applications of evolutionary algorithms in mechanical engineering".
20. Ka-Chun Wong, Kwong-Sak Leung, Man Hon Wong: Effect of Spatial Locality on an Evolutionary Algorithm for Multimodal Optimization. EvoApplications (1) 2010: 481-490
21. Ka-Chun Wong, Kwong-Sak Leung, Man Hon Wong: An evolutionary algorithm with species-specific explosion for multimodal optimization. GECCO 2009: 923-930
22. Ka-Chun Wong, Chun-Ho Wu, Ricky K.P. Mok, Chengbin Peng, Zhaolei Zhang, Evolutionary multimodal optimization using the principle of locality, Information Sciences, Volume 194, 1 July 2012, Pages 138-170, ISSN 0020-0255, 10.1016/j.ins.2011.12.016
23. Bagchi Tapan P (1999). "Multiobjective Scheduling by Genetic Algorithms". Multiobjective Scheduling by Genetic Algorithms. Kluwer Academic. ISBN 0-7923-8561-6.
24. Applying Genetic Algorithms to Recurrent Neural Networks for Learning Network Parameters and Architecture
25. 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.
26. "To the beat of the byte". BBC News. 1998-07-01. Retrieved 2010-05-03.
27. 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.
28. 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.
29. Wong, K., Leung, K., and Wong, M. 2010. Protein structure prediction on a lattice model via multimodal optimization techniques. GECCO 2010: 155-162
30. BiSNET/e – Distributed Software Systems Group, University of Massachusetts, Boston