128 results about "Genetic operator" patented technology

“In silico” nucleic acid recombination methods, related integrated systems utilizing genetic operators and libraries made by in silico shuffling methods are provided.

“In silico” nucleic acid recombination methods, related integrated systems utilizing genetic operators and libraries made by in silico shuffling methods are provided.

“In silico” nucleic acid recombination methods, related integrated systems utilizing genetic operators and libraries made by in silico shuffling methods are provided.

A present novel and non-trivial system, module, and method for constructing a flight path used by an avionics system are disclosed. A processor receives flight plan data and object data associated with terrain and obstacles. Free cells are extracted above the objects using a recursive space decomposition technique, and a reference path is formed through traversable free space determined from the availability of free cells. In an additional embodiment, threat data associated with hostile military weaponry and significant meteorological conditions could affect the availability of free cells. A genetic algorithm applying genetic operators which include mutators is employed with aircraft kinematic constraints to refine the reference path used to form a population of best path candidates. When a best path is reached after cycling through a re-generation process of path candidates, flight path data representative of the best path is generated and provided to at least one avionics system.

A subset of quality metrics as input to a disk drive failure prediction algorithm (DFPA) may be selected using a genetic algorithm. The DFPA is executed for an initially selected generation of subset quality metrics using quality metric values stored in a reference data base. At least one DFPA setting is adjusted and the DFPA executed again for the selected subset. After training the DFPA, the best DFPA setting is saved for the selected subset. A fitness score is generated for the selected subset, representing an accuracy of the DFPA relative to failure indicators stored in the reference data base. At least one genetic operator is applied in response to the fitness scores to generate a new generation of subsets. The process is repeated until a best subset of quality metrics and corresponding DFPA setting are found.

Exemplary embodiments of the present invention are directed to methods and program products for optimizing clustering of a design structure matrix. An embodiment of the present invention includes the steps of using a genetic operator to achieve an optimal clustering of a design structure matrix model. Other exemplary embodiments of the invention leverage the optimal clustering by applying a genetic operator on a module-specific basis.

A method of determining a set of parameters for modeling an active semiconductor device in which current flow through a channel or other area is regulated by voltage applied to the device terminals, for example, MOSFETs. The method comprises first providing a plurality of measured values for current as a function of voltage for a plurality of active semiconductor devices of differing geometries. There is then determined an initial population of vectors comprising individual values representing a plurality of desired active semiconductor device model parameters. Fitness is then evaluated for each of the vectors by comparing calculated values for current as a function of voltage from the population to the plurality of measured values for current as a function of voltage of the vectors, converting any current differences to voltage errors and adding any such voltage errors together to arrive at a fitness value for each vector. Vectors of best fitness are selected and at least one genetic operator is applied thereto to create a new population of the vectors. Vectors of best fitness are then selected. The steps of evaluating fitness and selecting vectors of best fitness are optionally repeated for such vectors of best fitness until a desired fitness is achieved to determine the desired active semiconductor device model parameters.

The invention relates to a mobile-robot route planning method based on an improved genetic algorithm. A raster model is adopted to preprocess a working space of a mobile robot, in a rasterized map, an improved rapid traversing random tree is adopted to generate connections of several clusters between a start point and a target point, portions for the mobile robot to freely walk on in the working space are converted into directed acyclic graphs, and a backtracking method is adopted to generate an initial population which is abundant in diversity and has no infeasible path on the basis of the directed acyclic graphs. Three genetic operators, namely a selection operator, a crossover operator and a mutation operator, are adopted to evolve the population, wherein the selection operator uses a tournament selection strategy, the crossover operator adopts a single-point crossover strategy, and the mutation operator adopts a mutation strategy which displaces an aberrance point with an optimal point in eight-neighbor points of the aberrance point. A quadratic b-spline curve is adopted to smooth an optimal route, and finally, a smooth optimal route is generated. According to the method, the route planning capability of the mobile robot under a complex dynamic environment is effectively improved.

The invention discloses a global optimization, search and machine learning method based on the Lamarckian principle of inheritance of acquired characteristics, comprising step 1: constructing an objective function ƒ(P) according to the problem being solved, where P represents a set of candidate solutions to the problem; step 2: encoding P into a genetic algorithm (GA) chromosome, inputting or automatically calculating algorithmic parameters of the GA, and initializing the algorithm and the population of candidate solution generation G0={P01, P02, . . . , P0S}, where S is the size of the population G and 0 stands for the initial generation; step 3: at generation k, optimizing the prevailing population of the candidate solutions Gk={Pk1, Pk2, . . . , PkS} iteratively using a Lamarckian “Heredity Operator” and a “Use-and-Disuse Operator” based on the values of ƒ(Gk); and step 4: outputting the final set of optimal solutions to the problem.

The invention provides an unmanned aerial vehicle route planning method based on an improved Salp algorithm, belonging to the technical field of unmanned aerial vehicle route planning. The method comprises the following steps: firstly, determining a start point position, a destination point position and a threatening area range; establishing a route planning cost model through path cost and threatening cost; performing optimizing for the established cost model, on the basis of a basic Salp algorithm, updating the position of a population with a sinusoidally varying iterative factor, embeddingan adaptive genetic operator to improve optimizing capability of the algorithm; after upper limit of iteration is reached, obtaining an optimal individual position, namely unmanned aerial vehicle optimal route points from the start point to the destination point; smoothening a connection line of the obtained optimal route points, obtaining the optimal route, and realizing route planning. The method provided by the invention can plan the optimal route from the start point to the destination point and avoid that the route is in the threatening area, the method has flexible, simple and fast calculation processes, and the method solves a problem that the existing route planning optimization algorithm has relatively low convergence speed and is very liable to be caught in local optimum.

The present invention provides a multi-unmanned aerial vehicle cooperation sequential coupling task distribution method of a mixing gravitation search algorithm, and relates to the unmanned aerial vehicle cooperation task distribution field. The method comprises: a multi-unmanned aerial vehicle cooperation task distribution model is constructed in the time coupling constraint, a fitness function and a task constraint are obtained, in the gravitation search algorithm based on genetic operators, the individual discretization coding and the population are initialized, the individual is decoded, and the fitness function is employed to calculate the fitness and perform individual update. Because the genetic operators are added in the gravitation search algorithm, the multi-unmanned aerial vehicle cooperation sequential coupling task distribution method of the mixing gravitation search algorithm has good general applicability, the number of times of long-term simulation tests and data statistics constructs a more improved database to allow the model to be more improved; and compared to the discrete particle swarm algorithm, the mixing gravitation search algorithm can be rapidly converged, the searching optimization result is optimal, the iteration process is brief, and the convergence speed is fast.

The invention discloses a genetic algorithm optimization-based software test data generation method, and belongs to the field of software testing. The method comprises the steps of performing static analysis on a current tested program to obtain a branch path coverage matrix; by considering the influence of layer proximity, branch distances and branch weights, designing a proper fitness function;in combination with an elite thought, improving a direction and a probability in a genetic operator of a genetic algorithm; selecting an initial population; replacing part of the initial population with a population comprising heuristic information and obtained by the coverage matrix; equally dividing the population; performing parallel genetic algorithm operation by using the improved fitness function and genetic operator; and selecting out optimal software test data meeting the conditions. The convergence speed of the genetic algorithm is increased while the algorithm is prevented from falling into local optimum, and the time cost of software test data generation is reduced.

The invention discloses a method for photovoltaic power station group region power prediction on the basis of an improved RBF neural network. The method comprises the steps of dividing a whole region into a plurality of sub-regions, performing data quality control, and removing data points remarkably not conforming to practice; calculating relative coefficients between photovoltaic power station measured power in every sub-region and sub-region total measured power, and selecting a plurality of standard photovoltaic power stations for every sub-region; utilizing a method of combining physics and statics to achieve short-term power prediction of the standard photovoltaic power stations; establishing a particle swarm optimization RBF neural network model based on a genetic operator, and predicting short-term power of every sub-region; and accumulating power prediction results of every sub-region to obtain prediction total power of a region photovoltaic power station group. The method aims to help a power scheduling department to formulate next day generation schedules according to the region power prediction results, real-time scheduling adjustment is optimized, spinning reserve capacity of a power system is reduced, system running costs are reduced, and the system photovoltaic acceptance capability is further improved.

The invention discloses a Dynamic service resource scheduling method based on A genetic-ant colony fusion algorithm. The method comprises the following steps: S1, establishing a service task, and determining a dynamic service resource set; s2, selecting a genetic operator, and carrying out solving on the basis of the genetic operator to obtain an optimized solution with a high fitness value; s3, selecting an ant colony operator, carrying out transition on the genetic operator and the ant colony operator, and converting an optimization solution solved by the genetic operator into initial pheromone distribution of the ant colony operator; and S4, obtaining a scheduling scheme of the dynamic service resources based on the initial pheromone distribution. According to the method, the ant colonyalgorithm and the genetic algorithm are fused and then applied to the scheduling problem of the dynamic service resources, the utilization rate of the dynamic service resources is increased, the resource use time, cost and the like are reduced, and the production efficiency is improved. The method provided by the invention has relatively high optimization solving capability, and the iterative convergence is better than that of other algorithms and tends to be relatively high in stability. The utilization rate of resources can be improved, and the economic benefits of enterprises are increased.

The present invention relates to a method for controlling motions of a robot using evolutionary computation, the method including constructing a database by collecting patterns of human motion, evolving the database using a genetic operator that is based upon PCA and dynamics-based optimization, and creating motion of a robot in real time using the evolved database. According to the present invention, with the evolved database, a robot may learn human motions and control optimized motions in real time.

The invention discloses a power distribution network planning method based on an improved genetic algorithm and a PRIM algorithm. The method comprises: establishing a power distribution network planning model; secondly, using an improved genetic algorithm to solve the optimal station address and number of the medium-voltage power distribution station and the capacity of the selected transformer, and enhancing the genetic algorithm by improving chromosome coding, a fitness function and a genetic operator; thirdly, using an improved PRIM algorithm to solve feeder line optimal paths between the high-voltage transformer substation and the medium-voltage transformer substation, between the medium-voltage transformer substation and the load center and between the medium-voltage transformer substation and the load center; fourthly, executing a power distribution network planning method based on an improved genetic algorithm and a PRIM algorithm on the test network to obtain an optimal arrangement planning scheme of the transformer substation and the medium-voltage feeder line, and determining an optimal power distribution network planning scheme by calculating economic and reliability indexes; and fifthly, performing load flow calculation by adopting a forward-backward sweep method to verify the practicability of the planning scheme. The method has the advantages of being high in searching speed and suitable for solving the planning problem of the large-planning power distribution network.

The invention proposes an improved genetic algorithm for flexible workshop scheduling, and the algorithm relates to the technical field of workshop scheduling, and specifically relates to the technical field of flexible workshop scheduling. The invention aims at the problems that a conventional genetic algorithm is complex in coding mode, is difficult for decoding, is weaker in search and development capability and is liable to be mature early and a non-feasible solution is liable to appear in the operation of a genetic operator. Compared with a conventional algorithm, the algorithm has the following improvements that 1, the coding is just performed on one chromosome, a coding chromosome gene consists of a ternary array (i, j, k), the coding mode is simple and convenient, and there is no need of decoding; 2, a positioning method is employed for selecting equipment for the process according to two different rules, and three known effective scheduling rule is employed for process arrangement; 3, crossing and mutation operations employ a genetic operator based on process priority protection; 4, before mutation, the probability of individual and genetic mutation is calculated through a formula, thereby achieving more accordance with the natural law. The algorithm is high in practicality.

The invention discloses a tolerance optimization allocation method based on a tolerance grade and a genetic algorithm, comprising the following steps: (1) creating a tolerance optimization mathematical model; (2) creating a standard tolerance database; (3) encoding based on the tolerance grade; (4) determining an adaptability function of the genetic algorithm; (5) setting a genetic operator; (6) performing genetic evolution; and (7) decoding to obtain a corresponding tolerance grade and inquiring a corresponding tolerance value. The method disclosed by the invention realizes the tolerance optimization allocation in the product design by using a computer, thereby replacing the manual distribution, improving the efficiency and saving the time; meanwhile, the method performs the optimization allocation through the genetic algorithm, serves the minimum processing cost as an optimization target and reduces the processing cost of products. Therefore, the method has good practical value and extensive application prospect in the technical field of computer aided tolerance design.

The invention discloses a multi-mode multi-standard path search method based on genetic algorithm, which is realized by computer. The method solves the multi-mode multi-standard traffic path search problem by defining genetic operator, matching corresponding strategy and utilizing the optimal theory. Under various trip standards, the method is used to solve, which does not have to set the weight value of each standard in advance, avoids no-solution risk brought by constraint method and reduces the influence of personal subjective factors. The path output result of the invention can satisfy various trip requirements and has the characteristic of various trip mode combinations, provides personalized and various trip projects for traveler and provides good technical support for the public trip information service.

The present invention discloses a disaster assistance ambulance path planning method based on a multi-agent genetic algorithm. The problem is solved that the ambulance path planning in disaster assistance is easy to fall in local optimum. The implementation steps of the method comprises: setting of an ambulance path; generating initial path planning through adoption of the multi-agent genetic algorithm; generating a new path planning through adoption of a genetic operator on an original scheme basis; searching a current global optimal path scheme in an agent grid through iteration; and if the iteration condition is satisfied, outputting the global optimal path scheme, or else, performing new iteration optimization until obtaining the global optimization, and outputting the path planning of the disaster assistance ambulance. The disaster assistance ambulance path planning method based on the multi-agent genetic algorithm is used for vehicle path planning of an ambulance in the disaster assistance, and employs the multi-agent genetic algorithm to take the latest service time as individual evaluation standard, design operations such as an effective coding mode and a local search operator and design an ambulance path planning scheme with high efficiency so as to improve the efficiency of the ambulance path planning.

The invention discloses an embedded software test data generating method based on a fuzzy-genetic algorithm and relates to a test data generating method. The problem that a test dataset generated with an existing test data generating method is large in scale, so that generating time is long is solved. A genetic algorithm is improved, a fuzzy control method is used, through population entropy and the disperse degree, selecting of a genetic operator in a genetic process is controlled in a self-adaptation mode, when population diversity becomes poor, crossover probability and mutation probability are enlarged, so that population is evolved in a global-optimum direction, and the scale of test data is decreased. Then, an ant colony algorithm is used for sorting the generated combination test data according to the large disperse degree, so that the distance between adjacent test data values is enlarged, and test data sorting with the large disperse degree are selected from the optimum path sorting of all combination test data and is used as final embedded software test data for outputting. The embedded software test data generating method is suitable for embedded software test data generating.

The invention relates to an X-structure Steiner tree construction method considering wiring resource relaxation. Five effective strengthening strategies are designed on the basis of a traditional right-angle Steiner tree. The method comprises the following steps: (1) changing a traditional wiring structure and adding a new wiring direction; (2) limiting the wiring length of the Steiner tree in theobstacle, and fully using wiring resources in the obstacle; (3) using a special particle swarm optimization algorithm combined with a classical genetic operator; (4) establishing a lookup table for providing rapid information query for the whole algorithm flow, and (5) designing two heuristic strategies of corner selection and refining to further improve the quality of the wiring tree. A simulation experiment of a standard test circuit shows that compared with the similar algorithm, the X-structure Steiner tree construction method considering the relaxation of the wiring resources makes fulluse of the wiring resources in the obstacle, effectively shortens the total wiring length and obtains the optimal total wiring length.

The invention relates to a meme evolution multiobjective optimization scheduling method based on objective importance decomposition, comprising following steps: randomly generating an initial population with volume of N; in every generation of the algorithm, selecting by the current population through binary championship, generating an offspring population by a genetic operator; carrying out fine search to the offspring population by a partial search strategy to obtain improved population; combining the current population, the offspring population and the improved population to generate a population, carrying out mutation operation to the individuals in the population with identical objective size; sorting the individuals in the population through using the rapid nondominated sorting and crowding distance method in the NSGA-II (Nondominated Sorting Genetic Algorithm II), thus selecting N best solutions as the next generation populations. According to the invention, a multiobjective flexible working shop system can be scheduled effectively; the scheduling effect is superior to the existing advanced algorithm; and the method of the invention can be widely applied in the computer application technical field and the production scheduling field.

A method for calculating the steering ratio at road cross based on genetic algorithm and maximum entropy model includes inputting flow rate data in certain time slot at study road cross and adjacent road cross, setting operator and control parameter of said algorithm and defining its search space and total frequency of solving solution, initializing calculation environment of said algorithm, executing genetic algorithm, calculating entropy of obtained solution and judging whether said total frequency is up or not if precision of obtained solution is satisfied and using solution corresponding maximum entropy as final solution if it is.

The invention discloses a real-time prediction system of a circulating fluidized bed household garbage burning boiler furnace outlet flue gas oxygen content and a method thereof. An integration modeling method of a support vector machine algorithm and a multi-population genetic particle swarm optimization algorithm is used. A rapid, economic and adaptive updating system and a method are constructed so as to predict a boiler furnace outlet flue gas oxygen content in real time, and tedious and complex mechanism modeling work is avoided. A nonlinear dynamical characteristic of a SVM algorithm, a generalization capability and a real-time prediction capability are used to represent a dynamical change characteristic of a flue gas oxygen content. A particle swarm optimization algorithm is used to optimize a SVM algorithm punishment parameter C and a nuclear parameter g so as to increase a generalization capability of a model. A genetic operator and a multi-population migration mechanism are introduced so as to accelerate a convergence speed of a particle swarm algorithm, increase a diversity of a particle swarm optimization algorithm solution, reduce a possibility for particle swarm algorithm optimization calculation to get into local optimum and increase a global search capability and a local search capability of the algorithm.

The embodiment of the present invention discloses a method and system for implementing network dynamic load balancing. The method includes: dividing a large-scale distributed system network into several local areas, and initializing the network topology population in the local area; Construct a multi-objective function according to multiple constraints in the local area, calculate the individual fitness of the individual in the population according to the multi-objective function, and select an individual with a higher fitness as the parent; perform a genetic operator on the parent , form offspring; Substitute the offspring into the multi-objective function to calculate its regret value, and judge whether the regret value of the offspring is less than the regret value of the parent generation; when the judgment result is yes, then use the The offspring replace the parent to form a new population; when the judgment result is no, the offspring are discarded. The system of the present invention can be used to implement the method. The technical scheme of the invention adopts a distributed strategy, the algorithm is simple, and has good adaptability.

The invention discloses a test case set generation method based on a combination chaotic sequence; the method comprises the following steps in sequence: a, using Chebyshev mapping and Logistic mapping to combine, thus forming the chaotic sequence; b, using the chaotic sequence to initialize population; c, setting an individual fitness function; d, defining a heredity operator; e, adding chaotic disturbance. The method uses the combination chaotic mapping to generate the initial population, each population individual comprises n genes, the population individual is a possible test case selection scheme, the combination chaotic sequence with uniform distribution characteristic is introduced to heredity algorithmic selection, intersect and variation operation, thus effectively preventing immature convergence, improving algorithmic global search capability, and calculating efficiency chaotic system generation initial population genes, adding chaotic small disturbance to each chaotic variable so as to carry out population optimization, and restraining to a proper individual through continuously evolution; test case minimization is converted into a method in which the individual with least genes is searched in the population, thus covering all test needs.

The invention discloses a chaos genetic algorithm based test case intensive simple algorithm. The chaos genetic algorithm based test case intensive simple algorithm comprises initializing male parent body codes; performing fitness calculation on a male parent body; defining genetic operators are defined, wherein the genetic operators comprise three steps of selection, intersection and variation, the genetic variation and the optimization are performed on the male parent body mainly to obtain a new male parent body finally, the change of the variation to an optimal solution can be increased due to the production of the new male parent body, and accordingly the fitness evaluation needs to be performed on the new male parent body after the genetic operators are finished to determine whether the output conditions are met or not, an optimal filial generation is output if yes, and the chaos disturbance is added if not; performing continuous iteration until the difference between fitness average values calculated through twice calculation is less than a preset minimum positive number epsilon 1. According to the chaos genetic algorithm based test case intensive simple algorithm, the algorithm is simple, the test efficiency can be improved, and the test cost can be reduced.