18 results about "Chromosome encoding" patented technology

The invention provides an equipment network comprehensive scheduling method based on an improved Jaya algorithm aiming at the condition that the migration time of working procedures between equipment is ignored in the existing flexible equipment network comprehensive scheduling method. The method is mainly divided into four parts. Wherein a double-chromosome coding method based on a process chain and an equipment chain is designed in the coding part, a coding method based on a quasi-schedulable process set and a random process coding method are designed in the process chain coding aspect, and the legality of a generated individual is ensured; in the aspect of the equipment chain, an equipment coding method based on short time and a random equipment coding method are designed to ensure that equipment distributed in the process is machinable equipment; a double-chain updating operation of updating a process chain first and then updating an equipment chain is designed in the updating part; in the decoding part, an equipment idle insertion decoding strategy considering migration time is designed; local search operation based on a critical path is designed in the local search part. The feasibility and superiority of the method are verified through experiments.

The invention discloses a county planting and breeding space collaborative optimization method, which comprises the following steps of: constructing an economic-ecological-planting and breeding coupled multi-target model, setting dynamic constraint conditions such as water resources and cultivated land, generating a Pareto non-inferior solution set by combining an NSGA-III algorithm, performing optimization such as chromosome coding and fitness calculation, and simulating a future land utilization pattern by utilizing a PLUS model. The method integrates multiple benefits, breaks through the limitation of a single target, and overcomes the defect that a traditional method is difficult to balance multiple targets; an optimization algorithm and a simulation model are deeply fused, a historical data trend is mined, and the problem of simulation and optimization disjunction is solved; through a fine constraint mechanism and optimization design, the method adapts to resource changes, improves accuracy, and provides scientific support for county planting and breeding space layout.

The application relates to a house interior design selection method, a terminal device and a storage medium, and the method comprises the following steps: setting a chromosome code composed of a plurality of room codes and a room position code, setting the value interval of each room code and room position code; setting the maximum value and minimum value of each gene on all individual chromosomes; randomly generating an initial population; according to the input mutation probability and the crossover probability, the population is iteratively updated through the mutation rule and the crossover rule; drawing the corresponding house plan of each individual in the kth generation population before and after the crossover; when the selection result of the better house plan is received, the individual chromosome corresponding to the selected better house plan is used as the individual in the k+1th generation population. The application can enable the user to participate in the design process of the indoor plan in a timely manner, and improve the design efficiency and the user satisfaction.

The invention relates to the technical field of software development, and discloses a method, a device and equipment for arranging and testing laboratory orders, and a storage medium. The method comprises the following steps: according to a current order arrangement strategy generated based on a reinforcement learning model, performing chromosome coding on current laboratory order information, current experimenter information and current experimental equipment information to obtain a corresponding initial population; the fitness of individuals in the initial population is evaluated based on a current fitness function, the initial population is iteratively evolved by selecting and improving crossover and mutation operation of chromosomes, and the current fitness function is obtained based on examination parameters of laboratory orders predicted by a first AI model; and under the condition that a set genetic termination condition is met, an optimal laboratory order scheduling and testing scheme is obtained. Therefore, through the reinforcement learning model and the AI model and in combination with the genetic algorithm, comprehensive optimization of laboratory order arrangement and measurement is realized, and the order arrangement efficiency and the resource utilization rate are improved.

This invention provides a hybrid coding and genetic operation method and system for optimizing the layout of pipe supports and hangers. The method includes: S1, determining the pipe segments to be optimized and combining the optimized pipe segments to form an optimization range; S2, selecting an appropriate optimization mode according to engineering requirements and design stage; S3, performing coding operations for each optimized pipe segment, and the combination of codes for all pipe segments forms a complete chromosome coding structure for the optimization range; S4, generating an initial parent population based on the chromosome coding structure, and performing genetic operations through crossover and mutation to generate offspring individuals; S5, decoding the offspring individuals, performing simulation analysis, screening and constructing a new generation population; S6, repeating the evolutionary process until a preset termination condition is met. This invention uses real number coding combined with integer coding, and introduces natural number index coding to establish a mapping relationship with the coding, and designs matching chromosome crossover and mutation operation rules to achieve efficient genetic operations on the coded chromosomes.

Use of one or more markers to identify and sort cells that have been engineered to comprise a synthetic chromosome encoding the marker(s), with said cells expressing said marker(s) encoded on the synthetic chromosome. The marker is typically but not limited to a cluster of differentiation (CD), wherein the CD has been truncated or otherwise modified so that the biological function of the CD is lost or remarkably reduced. The modified CD is typically used to sort cells to obtain a desired population of cells containing the synthetic chromosome, or i) to ensure that a cell population administered to a subject, e.g., suffering from cancer, reaches the target tissue, ii) to ensure that a cell population administered to a subject, e.g., suffering from cancer, stays at the desired site within the body of the subject during a treatment regimen, iii) to make it possible to turn on a kill switch comprised in the synthetic chromosomes of the cells of the cell population if the cell population is present at undesired sites in the body, or iv) to use as a diagnostic tool.

The present invention discloses a method for scheduling observation tasks of a terahertz telescope based on a genetic algorithm. Combining the specific constraints of the observation task scheduling of a space terahertz telescope, innovations are made in chromosome coding, population individual construction, and fitness function definition. It mainly includes seven functional parts: discretization of observation scheduling time, construction of scheduling chromosomes, calculation of fitness functions, construction of parents, genetic operations of crossover and mutation, screening of offspring, and obtaining the finally optimized scheduling individuals. By quantifying the constraints for observing terahertz target sources, population individuals corresponding to legal scheduling schemes are constructed. The observation completion degree of all target sources is used as the fitness function value of individuals and is quantified. The Roulette Wheel selection method is used to screen the parents for offspring inheritance, effectively reducing the number of generations and population size required for the scheduling optimization problem.

The invention discloses a genetic breeding strategy optimization method based on an evolutionary algorithm, and the method comprises the steps: S1, collecting and outputting a breeding multi-source data set, and obtaining the genotype and phenotype data of a parent group; s2, constructing a mathematical model of genetic breeding, and designing a chromosome coding scheme; s3, initializing an initial population, and outputting a fitness score of a breeding strategy by adopting an improved DeepFM model; s4, executing evolutionary iteration operation, and generating a filial generation population by adopting a CMA-ES algorithm; s5, judging whether a termination condition is met or not, if not, repeating iteration, and if yes, outputting an optimal solution breeding strategy; and S6, decoding the optimal solution breeding strategy, and outputting a specific breeding scheme. According to the method, the strategy potential is accurately evaluated through the improved DeepFM model, efficient optimization is carried out in combination with the CMA-ES algorithm, intelligence and automation of breeding decision making are achieved, and the breeding efficiency and the success rate of good variety breeding are remarkably improved.

The invention belongs to the field of management science and engineering, and particularly relates to a production plan optimization method based on a variable-length chromosome genetic algorithm. Comprising the following steps: firstly, obtaining to-be-arranged order data to form a to-be-arranged order data set; then, reading an order data set, initializing chromosome coding parameters and generating an initial population; decoding the chromosomes into a data format which can be identified by a simulation scheduling system, namely a production plan, performing simulation scheduling on the production plan by using the simulation scheduling system, calculating fitness values of the chromosomes and determining an individual with the highest fitness; and selecting an excellent individual as a parent according to a fitness result, generating a filial generation through crossover and mutation operation, updating the population, and repeating iteration until a convergence condition is met, so as to finally obtain a chromosome with the highest fitness and a corresponding production plan. By combining an intelligent optimization algorithm and a production scheduling simulation system, efficient decision support can be provided in the enterprise production plan making stage, the plan making speed and quality are remarkably improved, and the order achievement rate and the key resource utilization rate can be improved.

The application discloses a kind of transcription factor molecular marker based on regulating poultry myoblast fat metabolism and its application, the transcription factor is RXRA gene, the molecular marker is located on the chromosome where RXRA gene is, including the nucleotide sequence of the C5952908T mutation site and the upstream and downstream base group of the chromosome, the mutation site is linked with the C5952944T, A5952945G and T5952953C mutation site of the coding region of the subsequent chromosome.This application can judge poultry high feed efficiency individual by early molecular marker screening by identifying the genotype of transcription factor molecular marker based on regulating poultry myoblast fat metabolism existing in poultry genome, provides direct technical means for poultry feed efficiency selection, reduces feeding cost by early selection, and genetically improves feed conversion rate, accelerates the genetic progress of selection.

This invention provides a hybrid coding and genetic operation method and system for optimizing the layout of pipe supports and hangers. The method includes: S1, determining the pipe segments to be optimized and combining the optimized pipe segments to form an optimization range; S2, selecting an appropriate optimization mode according to engineering requirements and design stage; S3, performing coding operations for each optimized pipe segment, and the combination of codes for all pipe segments forms a complete chromosome coding structure for the optimization range; S4, generating an initial parent population based on the chromosome coding structure, and performing genetic operations through crossover and mutation to generate offspring individuals; S5, decoding the offspring individuals, performing simulation analysis, screening and constructing a new generation population; S6, repeating the evolutionary process until a preset termination condition is met. This invention uses real number coding combined with integer coding, and introduces natural number index coding to establish a mapping relationship with the coding, and designs matching chromosome crossover and mutation operation rules to achieve efficient genetic operations on the coded chromosomes.

The present invention provides a method for screening FSH response genes in sheep follicle granulosa cells based on a sheep whole genome CRISPR-Cas9 knockout library, and belongs to the field of genetic bioengineering technology. The present invention designs sgRNA probes for sheep whole genome coding genes, and constructs a sheep whole genome CRISPR-Cas9sgRNA library, focusing on knocking out sheep follicle granulosa cells with CRISPR-Cas9sgRNA libraries of sheep chromosomes 2, 3 and X coding genes, and constructs a specific knockout cell library of three chromosomes. Based on the cell knockout library, genes related to the action of FSH can be screened, and combined with transcriptome sequencing, the dose-dependent genes of FSH promoting the proliferation of granulosa cells are further explored.

The present disclosure provides a method and device for optimizing the arrangement of the transport path of a wind turbine generator set. The method includes: initializing a transport network according to the transport process nodes of various components, the transport process nodes including the shipping address, wind farm address and transshipment port address of various components; generating multiple chromosome individuals as an initial population through chromosome encoding according to the transport network, each chromosome individual including a feasible transport path for transporting each component from the shipping address to the wind farm address; calculating the fitness value of each chromosome individual in the initial population according to the fitness function; selecting chromosome individuals in the initial population whose fitness value is greater than a predetermined threshold to perform crossover and mutation operations until the number of iterations reaches the maximum evolutionary generation or the set convergence standard; outputting the chromosome individual with the maximum fitness value as the optimal solution; and performing chromosome decoding on the optimal solution to generate an arrangement optimization scheme.

Aspects of the present invention relate to synthetic chromosomes that may be incorporated into leukocytes, wherein the synthetic chromosomes comprise nucleic acid sequences encoding multiple Chimeric Antigen Receptors (CARs). Such manipulated leukocytes can be used in medicine, notably in the treatment of a cancer such as in the treatment of cancer having solid tumors. The leukocytes may be lymphocytes, including tumor-infiltrating lymphocytes. T cells. NK cells or B cells. In a preferred aspect, the leucocytes are syngeneic and T cells.

The invention relates to an optimization design method of a multilayer material radiation shielding scheme for a wood star radiation environment. The optimization design method comprises the following steps: step 1, analyzing wood star high-energy particle distribution based on an SPENVIS platform; step 2, selecting the type and superposition mode of the shielding material; step 3, genetic algorithm parameter design; step 4, carrying out undefined chromosome coding; 5, establishing a fitness function; and step 6, optimization design of an optimal multilayer material radiation shielding scheme is carried out. According to the method, the problem that it is difficult to consider multiple influence factors and obtain the optimal shielding scheme through a simple mathematical formula analysis method is solved, and theoretical guidance is provided for design and research of the radiation shielding scheme of the wooden star spacecraft.

The invention relates to the field of intelligent manufacturing and workshop scheduling, in particular to a flexible workshop efficient scheduling method fusing variable neighborhood search and an improved genetic algorithm. Comprising the following steps: 1) constructing a flexible job shop scheduling model; 2) designing segmented chromosome coding based on the flexible job shop scheduling model; 3) initializing a chromosome population; 4) performing adaptive crossover and mutation operation based on the chromosome population; 5) performing variable neighborhood search based on a key path on the chromosomes after crossover variation; and 6) circulating the step 4) and the step 5) until a set number of iterations is reached, and taking an obtained variable neighborhood search result as an optimal scheduling scheme. The method not only enriches the solving method of the flexible job shop scheduling problem, but also provides an effective solution for an enterprise to realize efficient and intelligent production scheduling, and has wide application prospect and popularization value.

This invention discloses a CPGA-SMCMN method for identifying single-drive pathways in cancer, comprising the following steps: 1. Setting up an SMCMN model; 2. Setting up CPGA: 2.1) Clustering; 2.2) Chromosome coding and initial population; 2.3) Fitness function; 2.4) Genetic operators; 2.5) Specific CPGA process. This method can identify more biological information, has strong scalability and practicality, and can identify more genes enriched in important drive pathways.

The present invention relates to the technical field of unmanned agricultural machinery, and specifically to a method and device for planning a full-coverage field path based on an improved genetic algorithm. The method comprises: S1, constructing a field environment map using a grid method. S2, performing chromosome encoding on the free grids in the field environment map, and randomly generating a number of initial chromosomes. S3, adjusting the initial chromosomes to obtain legal chromosomes. S4, constructing a fitness function based on a multi-objective equilibrium criterion, and determining the fitness of legal chromosomes. S5, evolving the initial chromosomes to generate the next generation population as the parent population for the next iteration. S6, determining whether to end this iteration. If so, decoding the chromosome output with the highest fitness in the contemporary population to obtain a full coverage path; if not, returning to execute step S3. The present invention can reduce the repeated operation area and the number of turns, and improve the field operation efficiency and operation quality of unmanned agricultural machinery.