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]
• Bioinformatics: RNA structure prediction^[5]
• Bioinformatics: Multiple Sequence Alignment^[6][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