358 results about "Mutation (genetic algorithm)" patented technology

The embodiment of the invention provides a customer classification method and device based on an improved particle swarm optimization algorithm, and the method comprises the steps: initializing a particle speed and a particle position according to a classification number and a feature dimension, and setting an initial value, so as to build an initial population of a particle swarm; performing iterative updating operation on the inertia weight, the particle speed and the particle position of the population according to a preset fitness function including the customer characteristic data until apreset iteration frequency is reached; after the number of iterations is preset, respectively carrying out selection operation, crossover operation and mutation operation on the particle swarm according to a genetic algorithm after each update for next iteration update until the iteration update reaches the total number of iterations or meets a convergence condition; and obtaining a clustering center according to the particle swarm reaching the total number of iterations or meeting the convergence condition, and classifying the customers. According to the method, through organic fusion of thegenetic algorithm, falling into a local optimal solution can be avoided, the later convergence speed is increased, and the search precision is improved.

The present invention discloses a method for combinatorial layout design, in particular family mold layout design, using a computer. The method starts with receiving design specific parameters and information about molding parts from user. A Genetic Algorithm module is then invoked to generate a population of layout designs which satisfies the design specific parameters. The Genetic Algorithm module first automatically generates a population of specially designed chromosome with three interdependent sessions. Crossover, mutation and replacement operation are applied on the population subsequently to evolve such towards a more optimal population over successive generations. In each generation step, a Genotype-Phenotype mapping module is utilized to decode the chromosome to corresponding layout design for fitness evaluation. A system for combinatorial layout design automation and optimization using this evolutionary design approach is also disclosed in the present invention.

The invention discloses an energy spectrum overlapping peak analysis method. The analysis method includes the steps that background rejection is carried out on energy spectrum sections which are obtained from radioactive measurement and to be subjected to overlapping peak resolution, and net peak areas of overlapping peaks and all channel net counts corresponding to the net peak areas of the overlapping peaks are obtained; the energy spectrum sections obtained after background rejection are regarded as a linear sum of multiple Gaussian functions; parameters of the Gaussian functions are combined into a chromosome; population initialization is carried out on the combined chromosome, probability construction fitness functions, from individuals, of the energy spectrum sections are combined, selection, cross and mutation operators of a genetic algorithm are adopted, the weight, average and standard deviation of all the Gaussian functions are obtained after multi-generation operation, and overlapping peak resolution is completed. Calculation is simple, the overlapping peaks overlapping three or more spectrum peaks can be resolved, and the overlapping peak resolving method can be effectively applied to qualitative and quantitative analysis for the spectrum peaks and is good in performance.

Provided is a method for selecting a broadcast routing path using a genetic algorithm in an Ad-hoc network. In the method, a plurality of nodes of the Ad-hoc network is defined as one chromosome, and the chromosome is represented with pairs of {order, power} in each node. Child nodes are created by performing an order based crossover and a power based crossover with respect to parent nodes neighboring to a source node. A mutation operation is performed with respect to the parent nodes and the child nodes. Relay nodes are determined by converting order and power information of each node of the chromosome into routing tree information. Power of the relay nodes is determined. A broadcast routing path is selected using the number and power of the relay nodes.

A field ready, unsupervised-use ready, method and apparatus for audio fitting a hearing aid is described in a hand held configuration having paired comparisons (hearing selections) stored in and derivable from a memory therein. The paired comparisons are presented one at a time to a user and a preferred selection for each paired comparison is made by a select indicator after the user toggles back and forth between the selections for as many times necessary in determining their preferences. A genetic algorithm converges all the preferences upon a single solution. Crossover and mutation genetic algorithm operators operate on a linear range of indexes representative of parametric values of the pairs. A fully integrated hearing aid having all the above described features incorporated therein is also presented.

The invention discloses a semiconductor workshop production scheduling method based on a genetic algorithm. The method comprises: (1) analyzing a semiconductor assembly line workshop scheduling problem; (2) determining the size of each individual matrix in combination with the encoding mode according to the processing time table of each workpiece process in a workshop; (3) initializing an intermediate variable under the condition that the optimal value is not improved; (4) carrying out crossing operation on any two individuals in the population; (5) combining new and old populations, and calculating the fitness value of each individual; (6) judging whether Q' and Q are the same or not; (7) executing selection operation on the merged population; (8) judging whether r or n meets a termination criterion or not; (9) judging whether the individuals meet mutation operation or not; (10) returning the mutated population to the step (4) for operation when the other n is equal to n+1; and (11) outputting the optimal individual of the population. According to the invention, the scheduling problem of complex flexible flow production workshops in the semiconductor industry is solved; and multiple unnecessary calculation processes due to the fact that the maximum number of iterations is set to be too large are avoided, the algorithm calculation time can be shortened, and the efficiency is improved.

A multi-target flexible job shop scheduling method based on a double-layer genetic algorithm is characterized in that a traditional genetic algorithm is improved, so that a population optimized by thegenetic algorithm within limited time has high quality; meanwhile, the solving mode of the traditional genetic algorithm for the multi-target problem is changed, a double-layer solving framework is provided, and compared with the traditional genetic algorithm, the solving quality is obviously improved. According to the method, the crossover strategy and the mutation strategy are optimized, and the selection operator is deleted. The improved genetic algorithm is combined with a rapid non-dominated sorting and congestion degree calculation module, a special process framework is designed for theimproved genetic algorithm, and the improved genetic algorithm is called as a double-layer genetic algorithm. According to the algorithm, an excellent non-dominated solution set can be obtained within limited time, and the algorithm has good practicability and can be well applied to actual workshop scheduling.

Software built in self test computer programs 12 are generated using a genetic algorithm 14 technique. A fault simulator 20 is used to simulate candidate software built in self test computer programs and compare the simulated execution, such to deliberately introduced test faults, with expected execution outcomes previously derived for that candidate program to determine the sensitivity of that candidate program to the faults which are introduced. This score can be fed back into the genetic algorithm mutation to converge the mutation process upon appropriately fault sensitive software built in self test program code.

The invention discloses a human body composition analysis method based on a genetic algorithm. The human body composition analysis method based on the genetic algorithm comprises the following steps: eight sections of human body impedance models are selectively used and an expression of each section of human body impedance is analyzed and calculated; groups of different voltage and current are set so that groups of human body impedance data models are obtained through calculation; an AIC value of each group of human body impedance data models is calculated through an akaike information criterion and combination with human body physiological parameters; a fitting model is selected; and genetic evolution is conducted on a position coefficient of the fitting model, an unknown parameter of the fitting model is determined through copy, intersection and mutation operation and a human body composition predicting formula is obtained. According to a calculation method of the eight sections of human body impedance models, theoretical reference can be provided to eight section impedance measurement technology. According to the human body composition predicting method based on the genetic algorithm, human body composition predicting accuracy can be improved and an effective detection measure is offered for human body composition research and clinical application.

The invention provides a construction project multi-objective optimization method. The construction project multi-objective optimization method comprises the following steps: determining a mathematical model and genetic algorithm parameters of multi-objective optimization; establishing a population with feasible constraints and a population target function matrix; calculating an objective weight of the target function by adopting an entropy weight method according to the target function matrix, and synthesizing a hybrid dynamic weight of the target function; sorting the population by adoptinga method based on dynamic weight to obtain a Pareto temporary solution set; attaching virtual fitness values to individuals according to population individual sorting, and selecting a filial generation population by adopting a proportional selection operator and a roulette method; performing crossover operation on the filial generation population; performing mutation operation on the filial generation population after the crossover operation; combining the Pareto temporary solution set with the filial generation population after mutation operation to generate a new population; and if the algorithm termination condition is met, terminating the algorithm, otherwise, returning. According to the method, the problem of ambiguity between an original multi-objective optimization algorithm and engineering application is well solved, and the method has better engineering applicability.

The invention discloses an optimization method for vehicle path planning of an assembly type construction site. The method comprises the following steps: defining important elements in a fabricated construction site vehicle path optimization problem, setting parameters, and converting two-dimensional coordinates of a construction site into an Euclidean distance matrix; running an ant colony algorithm, performing probability operation of selecting a next access stacking point according to a roulette selection algorithm, and continuously performing pheromone updating; and, according to the genetic algorithm, encoding three important parameters alpha, beta and rho in the ant colony algorithm which are used as dyes, after the optimal combination of alpha, beta and rho is obtained through crossover and mutation operation, taking the optimal combination as an input parameter and then substituting the input parameter into the ant colony operation, and after finite iteration is performed, finally obtaining the optimal path of the vehicle. According to the method, the iterative performance and the optimization efficiency of the model are improved, a local optimal solution is prevented frombeing obtained, and meanwhile, the convergence speed of the model is increased, so that the method is greatly helpful to the scheduling optimization problem of prefabricated part transport vehicles ina prefabricated building site.