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This is a list of Genetic Algorithm (GA) applications ==Applications of genetic algorithms== * Airlines Revenue Management.〔Aloysius George, B. R. Rajakumar, D. Binu,(2012) "Genetic algorithm based airlines booking terminal open/close decision system". ()〕 * 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 and () a tutorial on genetic particle models) * Bioinformatics multiple sequence alignment. * Bioinformatics: RNA structure prediction. * Bioinformatics: Multiple Sequence Alignment. SAGA is available on:.〔(Cedric Notredame Home Page )〕 * Bioinformatics: Motif Discovery.〔(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. )〕 * Biology and computational chemistry (() links to particle methods in biology and computational chemistry and () an article on genetic particle models) * Building phylogenetic trees. * 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.〔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〕 * Code-breaking, using the GA to search large solution spaces of ciphers for the one correct decryption.〔Joachim De Zutter〕 * Computer architecture: using GA to find out weak links in approximate computing such as look-ahead. * Computer-automated design 〔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/〕 * 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. * Container loading optimization. * Control engineering,.〔(Ng, K.C., Switching Control Systems and Their Design Automation via Genetic Algorithms, PhD Thesis, University of Glasgow, 1995. )〕〔(K. Michail, Optimised Configuration of Sensing Elements for Control and Fault Tolerance Applied to an Electro-Magnetic Suspension, PhD Thesis, Loughborough University, UK, 2009. )〕 * Data Center/Server Farm.〔(SymbioticSphere – Distributed Software Systems Group, University of Massachusetts, Boston )〕 * Design of water resource systems *Design of anti-terrorism systems * Distributed computer network topologies. * Electronic circuit design, known as evolvable hardware. * Gene expression profiling analysis. * Feynman-Kac models (() links to genetic type particle interpretations, () a review article on genetic particle models, and a research monograph ()) * Financial Mathematics (() links to particle methods in mathematical finance and () a tutorial on genetic particle models) * File allocation for a distributed system. * Filtering and signal processing (() links to particle filters and () a tutorial on genetic particle models) * Finding hardware bugs.〔Hitoshi Iba, Sumitaka Akiba, Tetsuya Higuchi, Taisuke Sato: BUGS: A Bug-Based Search Strategy using Genetic Algorithms. PPSN 1992:〕〔Ibrahim, W. and Amer, H.: An Adaptive Genetic Algorithm for VLSI Test Vector Selection〕 * Game theory equilibrium resolution. * Genetic Algorithm for Rule Set Production * Economics * Scheduling applications, including job-shop scheduling and scheduling in printed circuit board assembly. 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. * Groundwater monitoring networks〔(Optimization of Water-level Monitoring Networks in the Eastern Snake River Plain Aquifer Using a Kriging-based Genetic Algorithm Method ) United States Geological Survey〕 * Learning robot behavior using genetic algorithms. * Image processing: Dense pixel matching.〔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〕 * 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 * Mobile communications infrastructure optimization. * Molecular structure optimization (chemistry). * Multidimensional systems * Multimodal Optimization 〔(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 )〕〔(Ka-Chun Wong, Kwong-Sak Leung, Man Hon Wong: An evolutionary algorithm with species-specific explosion for multimodal optimization. GECCO 2009: 923-930 )〕〔(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 )〕 * Multiple criteria production scheduling. * Multiple population topologies and interchange methodologies. * Mutation testing * Neural Networks; particularly recurrent neural networks〔(Applying Genetic Algorithms to Recurrent Neural Networks for Learning Network Parameters and Architecture )〕 * Operon prediction. * 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. * Power electronics design.〔(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. )〕 * Protein folding and protein/ligand docking.〔(Wong, K., Leung, K., and Wong, M. 2010. Protein structure prediction on a lattice model via multimodal optimization techniques. GECCO 2010: 155-162 )〕 * Quality control * Rare event analysis (() links to particle rare event simulation and () a review article) * Real options valuation * 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 (() 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 and its applications.〔 * Vehicle routing problems with multiple soft time windows, multiple depots and an heterogeneous fleet * Wireless sensor/ad-hoc networks.〔(BiSNET/e – Distributed Software Systems Group, University of Massachusetts, Boston )〕 ==References== 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「List of genetic algorithm applications」の詳細全文を読む スポンサード リンク
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