• Source: List of genetic algorithm applications

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


      Natural Sciences, Mathematics and Computer Science


      Bayesian inference links to particle methods in Bayesian statistics and hidden Markov chain models
      Artificial creativity
      Chemical kinetics (gas and solid phases)
      Calculation of bound states and local-density approximations
      Code-breaking, using the GA to search large solution spaces of ciphers for the one correct decryption.
      Computer architecture: using GA to find out weak links in approximate computing such as lookahead.
      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.
      Data Center/Server Farm.
      Distributed computer network topologies
      Electronic circuit design, known as evolvable hardware
      Feature selection for Machine Learning
      Feynman-Kac models
      File allocation for a distributed system
      Filtering and signal processing
      Finding hardware bugs.
      Game theory equilibrium resolution
      Genetic Algorithm for Rule Set Production
      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.
      Learning robot behavior using genetic algorithms
      Image processing: Dense pixel matching
      Learning fuzzy rule base using genetic algorithms
      Molecular structure optimization (chemistry)
      Optimisation of data compression systems, for example using wavelets.
      Power electronics design.
      Traveling salesman problem and its applications
      Stopping propagations, i.e. deciding how to cut edges in a graph so that some infectious condition (e.g. a disease, fire, computer virus, etc.) stops its spread. A bi-level genetic algorithm (i.e. a genetic algorithm where the fitness of each individual is calculated by running another genetic algorithm) was used due to the ΣP2-completeness of the problem.


      Earth Sciences


      Climatology: Estimation of heat flux between the atmosphere and sea ice
      Climatology: Modelling global temperature changes
      Design of water resource systems
      Groundwater monitoring networks


      Finance and Economics


      Financial mathematics
      Real options valuation
      Portfolio optimization
      Genetic algorithm in economics
      Representing rational agents in economic models such as the cobweb model
      the same, in Agent-based computational economics generally, and in artificial financial markets


      Social Sciences


      Design of anti-terrorism systems
      Linguistic analysis, including grammar induction and other aspects of Natural language processing (NLP) such as word-sense disambiguation.


      Industry, Management and Engineering


      Audio watermark insertion/detection
      Airlines revenue management
      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, including research on composite material design and multi-objective design of automotive components for crashworthiness, weight savings, and other characteristics
      Automated planning of structural inspection
      Container loading optimization
      Control engineering,
      Marketing mix analysis
      Mechanical engineering
      Mobile communications infrastructure optimization.
      Plant floor layout
      Pop music record production
      Quality control
      Sorting network
      Timetabling problems, such as designing a non-conflicting class timetable for a large university
      Vehicle routing problem
      Optimal bearing placement
      Computer-automated design


      Biological Sciences and Bioinformatics


      Bioinformatics Multiple Sequence Alignment
      Bioinformatics: RNA structure prediction
      Bioinformatics: Motif Discovery
      Biology and computational chemistry
      Building phylogenetic trees.
      Gene expression profiling analysis.
      Medicine: Clinical decision support in ophthalmology and oncology
      Computational Neuroscience: finding values for the maximal conductances of ion channels in biophysically detailed neuron models
      Protein folding and protein/ligand docking
      Selection of optimal mathematical model to describe biological systems
      Operon prediction.


      General Applications


      Neural Networks; particularly recurrent neural networks
      Training artificial neural networks when pre-classified training examples are not readily obtainable (neuroevolution)


      Physics


      Optimization of beam dynamics in accelerator physics.
      Design of particle accelerator beamlines


      Other Applications


      Clustering, using genetic algorithms to optimize a wide range of different fit-functions.
      Multidimensional systems
      Multimodal Optimization
      Multiple criteria production scheduling
      Multiple population topologies and interchange methodologies
      Mutation testing
      Parallelization of GAs/GPs including use of hierarchical decomposition of problem domains and design spaces nesting of irregular shapes using feature matching and GAs.
      Rare event analysis
      Solving the machine-component grouping problem required for cellular manufacturing systems
      Stochastic optimization
      Tactical asset allocation and international equity strategies
      Wireless sensor/ad-hoc networks.


      References

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