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]
• 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.^[5]
• 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.^[6]
• Code-breaking, using the GA to search large solution spaces of ciphers for the one correct decryption.^[7]
• Computer-automated design ^[8]
• Configuration applications, particularly physics applications of optimal molecule configurations for particular systems like C₆₀ (buckyballs).
• Container loading optimization.
• Control engineering,.^[9][10][11]
• Data Center/Server Farm.^[12]
• Design of water distribution systems.
• Distributed computer network topologies.
• Electronic circuit design, known as evolvable hardware.
• Gene expression profiling analysis.^[13]
• 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.^[14][15]
• 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^[16][17]
• Mobile communications infrastructure optimization.
• Molecular structure optimization (chemistry).
• Multidimensional systems
• Multimodal Optimization ^[18][19]
• Multiple criteria production scheduling.^[20]
• Multiple population topologies and interchange methodologies.
• Mutation testing
• Neural Networks; particularly recurrent neural networks^[21]
• Operon prediction.^[22]
• 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.^[23]
• Power electronics design.^[24]
• Protein folding and protein/ligand docking.^[25][26]
• 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
• 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.^[27]

References