20 results about "Genetic programming algorithm" patented technology

Disclosed in the present invention is an image classification method based on collaborative optimization algorithms and a feature selection mechanism, which effectively improves the image feature extraction quality and the image classification accuracy. The technical solution comprises: step S1, preprocessing a collected image; step S2, constructing an image feature extraction model on the basis of a genetic programming algorithm, performing image feature extraction, using the concept of individual information optimization to assign position and velocity information to each individual in the algorithm, and updating the position and velocity information of each individual to adjust a selection operator; step S3, constructing a feature selection model to perform selection on the extracted features; step S4, by using the selected features as inputs, training an SVM classifier; and step S5, using the trained SVM classifier to classify express delivery images.

The invention relates to a multi-unmanned aerial vehicle (UAV) search strategy optimization method based on genetic programming (GP), which is characterized in that under a distributed model predictive control (DMPC) framework, each UAV is guided by a target function to realize a search trajectory optimization process, and essentially, a search strategy is converted into a mathematical logic mapping relation. In a traditional method, objective function construction mainly depends on a design thought dominated by artificial experience, and the fixed search mode based on subjectivity has the limitations of insufficient environmental adaptability, weak strategy generalization ability and the like in a complex dynamic environment. In order to solve the problem, the invention provides a search strategy optimization method based on a genetic programming algorithm, and online dynamic evolution and parameter adaptive adjustment of a search strategy are realized by establishing a dynamic mapping model between a strategy parameter space and a search performance index.

The invention relates to the technical field of interface calling, and provides an interface scheduling optimization method and device, equipment, a storage medium and a program product, and the method comprises the steps: carrying out the gene coding of each to-be-called application interface, and obtaining the gene sequence of each application interface; the gene sequence corresponds to a scheduling scheme of the application interface; generating a population based on the gene sequence, and constructing a fitness function according to the evaluation index of the application interface; the evaluation indexes comprise a bandwidth utilization rate, a calling success rate and response duration; performing iterative optimization on the population based on the fitness function by adopting an improved genetic programming algorithm to obtain an optimal individual; and performing scheduling optimization on the application interface based on the target scheduling scheme corresponding to the optimal individual. Through gene coding and an improved genetic programming algorithm, intelligent optimization of an interface scheduling scheme is realized, manual intervention is reduced, optimization efficiency and accuracy are improved, and an optimization effect is ensured.

The invention relates to the technical field of hazardous chemical substance safety management, and discloses a hazardous chemical substance physicochemical property management system and method.The hazardous chemical substance physicochemical property management method comprises the steps that hazardous chemical substance physicochemical property data are preprocessed, and a standardized data set is obtained; carrying out feature extraction and transformation; pre-learning a feature extraction and transformation result by using a deep neural network containing physical consistency constraint, and extracting a complex mode in the data; self-adaptive symbol regression is carried out, wherein expression search space is defined, nerve-guided search space pruning is carried out, and an improved genetic programming algorithm is applied; carrying out optimization and verification, and selecting an optimal mathematical expression as a prediction model of the physicochemical properties of the hazardous chemical substances; according to the method, through a mathematical expression generation technology based on symbol regression, high precision and interpretability of the prediction model are realized, and a user can intuitively understand a calculation process and physical significance of a prediction result.

The invention relates to an intelligent power transformation micro-station equipment energy consumption prediction system and method based on a symbol regression algorithm, and the method comprises the steps: selecting daily average temperature, daily average humidity, sunshine duration t and traffic flow VF as four input features which affect the daily total energy consumption of an intelligent power transformation micro-station; three modules of a symbol regression algorithm based on adaptive iteration and control variable genetic programming: an adaptive iteration module, a control variable module and a genetic programming algorithm module are used for training a standard genetic programming algorithm; and inputting the sorted historical energy consumption data set into an intelligent power transformation micro-station equipment energy consumption prediction model based on a symbol regression algorithm to obtain a total energy consumption prediction value of the intelligent power transformation micro-station on the day. The method can effectively mine the potential relation between the characteristics in the data set, obtains the displayed prediction equation, and accurately predicts the energy consumption of the intelligent power transformation micro-station equipment. The search space of the algorithm can be reduced, the training time of the prediction model is shortened, and the model energy consumption prediction precision is improved.

A small sample image classification method based on hierarchical learning genetic programming algorithm, comprising the steps of: 1: constructing a small sample image classification system based on hierarchical learning genetic programming algorithm; 2: the image acquisition module acquires the image data set and divides it into a training set and a test set; 3: the PEGP module acquires the training set and performs image preprocessing and feature extraction operations on it to construct a feature storage table; 4: the DEGP module takes the features in the feature storage table as the terminal input, constructs an integrated solution, and optimizes the final classification effect through an integrated strategy based on individual difference values; 5: use the test set as the input of the image classification solution, output the predicted class label of the test set, and evaluate the performance of the image classification solution according to the actual label of the test set; 6: take the image data to be classified as the input of the image classification solution and output the image classification result. Effect: good classification effect can be achieved under the condition of limited sample quantity.

The invention provides a reservoir ex-reservoir sand content prediction method based on multi-tree genetic programming, and relates to the technical field of hydraulic engineering, and the method comprises the following steps: S1, data collection and preprocessing: collecting daily scale water and sand of a reservoir and geometric feature data of the reservoir, and carrying out the normalization processing; s2, constructing a multi-tree genetic programming model, wherein the model is composed of symbol trees; s3, an objective function is defined, the objective function is a linear weighting function, and decision coefficients and complexity are comprehensively considered; s4, performing model training and evolution, training a multi-tree genetic programming population through a genetic programming algorithm and a training set, and evaluating population fitness; s5, model verification and evaluation: verifying the trained model by using a verification set; s6, outputting a prediction result; according to the method, through combination of multi-tree genetic programming and a linear weighted objective function, the model can simultaneously ensure high prediction precision and high generalization ability, and through integration of multiple symbol trees, the nonlinear relationship of the reservoir water-sediment process can be accurately captured.

The application provides a dynamic tug scheduling method using a multi-population multi-objective genetic programming algorithm, and comprises the following steps: establishing a simulation environment, defining a target function to be solved, and defining total flow time and total cost of tug operation as two optimization objectives; solving the target function to be solved through the multi-population multi-objective genetic programming algorithm to obtain an optimal decision rule individual; and calling allocation rules and sequence rules of the selected decision rule individual to make a tug scheduling decision according to a ship arrival event and an operation preparation event. The application improves the flexibility of ship scheduling, thereby improving the port operation efficiency and reducing the operation cost.

The application discloses a kind of dynamic flexible workshop scheduling methods based on multi-rule combination, the method is divided into two processes of scheduling rule generation and scheduling rule combination for scheduling problem, first by genetic programming algorithm, automatically generate scheduling rule for current working condition, and save the well performance scheduling rule therein;Then based on a kind of hybrid differential evolution algorithm, a plurality of good scheduling rules are combined and optimized, to achieve better scheduling effect.The application realizes the combination optimization based on multi-scheduling rule in the scene of industrial workshop scheduling, breaks the limitation that only state quantity without weight coefficient in the scheduling rule generated based on genetic programming in tradition, compared with single scheduling rule, the combined scheduling rule has stronger generalization and better scheduling effect.

The invention discloses a three-tree genetic programming method based on multiple constraint objectives, which is used for solving a task flow scheduling problem in a computing power network environment, and comprises the following steps: (1) building a distributed netlike computing power network environment model; (2) obtaining a task flow structure and a task data set; (3) respectively designing genetic programming terminals according to scheduling rules generated by three-tree genetic programming, and initializing a three-tree genetic programming population; (4) designing a simulator, simulating an execution process of the workflow based on a scheduling rule generated by three-tree genetic programming in a simulation environment, and evaluating load balance and task flow average completion time by taking a task flow success rate as a hard constraint; (5) designing a constraint multi-objective genetic programming algorithm to update the population; and (6) outputting a non-dominated genetic programming scheduling rule set after a preset number of iterations is reached. According to the method, task flow scheduling simulation of a computing power network environment is realized, and compared with a traditional method, the performance of constrained multi-objective optimization is improved.

The invention belongs to the related technical field of workshop scheduling, and discloses a two-stage batch flow flexible job workshop dynamic scheduling method and equipment, and the method comprises the steps: (1) extracting product processing technology features to construct a multi-dimensional batch feature set, carrying out the automatic selection and combination of batch features in the multi-dimensional batch feature set through employing a genetic programming algorithm, and carrying out the automatic selection and combination of the batch features in the multi-dimensional batch feature set; generating a plurality of batch scheduling rules through evolution operation, wherein the plurality of batch scheduling rules form an initial batch scheduling rule set; and (2) carrying out weight dynamic optimization on the initial batch scheduling rule set by adopting a deep reinforcement learning algorithm, and then determining a current optimal workpiece sorting and machine selection strategy so as to obtain a scheduling scheme. Through cooperative work of the genetic programming algorithm and the deep reinforcement learning algorithm, real-time response and adaptive scheduling of dynamic disturbance such as new workpiece arrival and machine faults in the production process are realized, the optimization efficiency is effectively improved, and the workshop production efficiency and the resource utilization rate are improved.

This invention discloses an efficient method for constructing health indicators for rolling bearings, comprising: sampling monitoring data of the entire life cycle of rolling bearings; extracting primary features of the entire life cycle of rolling bearings and using them as input to generate an initial chromosome population; optimizing the output of higher-level features of the chromosomes based on evaluation function one; obtaining a higher-level feature population by outputting higher-level features of the chromosomes; evaluating the higher-level feature population constructed by the current chromosome population based on evaluation function two; if a termination condition is met, proceeding to the next step; otherwise, performing genetic operations on the current population according to evaluation function two, repeating the above steps until the termination condition is met; after meeting the termination condition, outputting the chromosome with the highest fitness in the final generation population, and using its corresponding higher-level feature as a health indicator reflecting the degradation trend of rolling bearings. This invention can solve the problem that traditional genetic programming algorithms cannot efficiently search for health indicators that effectively characterize the degradation trend of rolling bearings.

The application discloses a GP-based hierarchical unmanned cluster task planning method and device, equipment and medium, constructs a hierarchical task individual tree with structured semantics; the strong type genetic programming algorithm is used as a population evolution baseline, the hierarchical task individual tree is input into the strong type genetic programming algorithm to generate an initial population, the performance of each initial individual is evaluated, and an adaptability evaluation result is obtained; it is judged whether the termination condition is not met according to the adaptability evaluation result, each individual is selected, crossed and mutated by using the strong type genetic programming algorithm, redundant nodes in each individual are cleaned by using a pruning algorithm based on domain knowledge in the crossing and mutation operation process, and the optimal hierarchical task individual tree is converted into a task planning scheme of an unmanned cluster cooperative system. The application solves the technical problems of unclear task structure expression ability, imperfect feasibility guarantee mechanism, inefficient search and non-adaptability in the existing unmanned cluster cooperative task planning method.

The invention provides a dynamic tug scheduling method adopting a multi-population multi-objective genetic programming algorithm. The method comprises the following steps: establishing a simulation environment, defining a to-be-solved objective function, and defining total process time and total cost of tug operation as two optimization objectives; solving the target function to be solved through a multi-population multi-target genetic programming algorithm to obtain an optimal decision rule individual; and according to the ship arrival event and the operation preparation event, calling the distribution rule and the sequence rule of the decision rule individuals to carry out tug scheduling decision. According to the invention, the flexibility of ship scheduling is improved, so that the port operation efficiency is improved, and the operation cost is reduced.

The invention relates to the field of chemical rectification, and discloses a composite rectification sequence optimization method which comprises the following steps: acquiring an initial mixture to be rectified and a group of composite rectification sequences for rectifying the initial mixture; based on a genetic programming algorithm, converting the composite rectification sequence into an initial tree species group; performing iterative optimization on the initial tree species group on the basis of a tournament algorithm to obtain an optimized tree species group, and performing iterative optimization on nodes of the initial tree species group on the basis of an exhaustion method or an elite genetic algorithm to obtain a first optimal node; based on an exhaustion method or an elite genetic algorithm, iteratively optimizing nodes of the optimized tree species group to obtain a second optimal node; updating the composite rectification sequence into an optimized rectification sequence; and updating the initial rectification unit of the composite rectification sequence to the optimized rectification unit. According to the method provided by the embodiment of the invention, the rectification sequence optimization efficiency is improved, and the global optimality of the rectification unit is ensured.

This invention discloses a method for optimizing and reconstructing bridge scour formulas based on symbolic regression, comprising the following steps: obtaining statistical data on bridge scour, which are used as training and testing sets respectively; determining the initial structure of the bridge scour formula; firstly determining candidate operators, then introducing a group of functions with significant physical meaning and statistical relationships in scour to determine candidate operational variables; using a symbolic regression method based on genetic programming to generate an optimized formula using the data from the training set; adjusting parameters to generate a group of candidate formulas; evaluating the formula performance based on the testing set, and selecting the formula form with the best prediction effect and the simplest formula structure. This invention, based on existing standard formulas, uses a symbolic regression method based on genetic programming algorithms, integrating machine learning methods with nonlinear representation capabilities to more accurately mine rich features in the data. Simultaneously, by combining prior knowledge of scour and physical relationships, it can improve the predictive performance and generalization of the scour calculation formula.

The invention relates to a large cylinder grain size prediction method based on improved genetic programming, and the method comprises the steps: firstly constructing a large cylinder grain size data set, carrying out the data preprocessing, then building a symbolic regression model based on an improved genetic programming algorithm, innovatively embedding physical constraints in genetic manipulation, scanning all operator nodes, and carrying out the prediction of the grain size of a large cylinder. Replacing the part which does not conform to the physical constraint according to a correction mechanism, adding a pinning operator in the physical constraint, and introducing a dynamic complexity control item in a fitness function to ensure that the generated mathematical expression has high precision, physical rationality and conciseness; and finally, outputting an explicit calculation formula of the relationship between the grain size and the input parameters and a grain size predicted value. According to the method, high-precision prediction of the grain size under multi-source data fusion driving is achieved, the experiment cost and the research and development period are remarkably reduced, the generated expression has high interpretability, and a new view angle is provided for material mechanism research.

This invention discloses a method for predicting the microbial corrosion sensitivity and corrosion behavior of metallic materials based on symbolic regression, belonging to the field of material corrosion prediction technology. It is based on the relationship between Ti / Cu similar alloy systems and microorganisms... Shewanella algae A microbial corrosion dataset was constructed, and the input features and operators of a symbolic regression model were screened and determined. A microbial corrosion sensitivity index, which can compare the severity of microbial corrosion across material systems, was defined as the target attribute. A symbolic regression model based on a genetic programming algorithm was established, and the prediction results of microbial corrosion sensitivity were obtained based on the function expression output by the symbolic regression model. Based on the definition of microbial corrosion sensitivity, the prediction of microbial corrosion behavior was achieved through simple characterization of intrinsic material properties and aseptic corrosion testing. This invention utilizes symbolic regression to explore the relationship between intrinsic material properties and microbial corrosion sensitivity.

The application discloses a routing protocol inversion method based on symbolic regression and explainable machine learning, and relates to the technical field of network protocol reverse engineering and machine learning cross technology. In view of the network cognition problem caused by the opacity of a private routing protocol, multi-temporal network observation data are acquired, and multi-temporal observation data such as network topology, link features and routing tables are collected. A causal discovery algorithm is used to identify key link features that have a direct causal impact on routing decisions. A graph attention network is constructed and trained, and the contribution of each feature is calculated by analyzing the attention weight and gradient information. A genetic programming algorithm is used to search for a routing metric function based on the key link features and the contribution ranking. The routing strategy is extracted based on the routing metric function. The application combines the expression discovery capability of symbolic regression with the graph neural network structure modeling capability, realizes accurate inversion and explainable expression of the unknown routing protocol metric function and routing strategy, and the inversion result has strong explainability.

The present application relates to the field of customer portrait construction, in particular to a customer portrait construction system based on machine learning, comprising a multi-source data acquisition module, a multi-modal feature extraction module, a customer portrait representation learning module and a graph portrait output module, the multi-source data acquisition module integrates data after hash desensitization and graph anomaly detection processing; the multi-modal feature extraction module generates the highest fitness combination features through genetic programming algorithm, forms the portrait feature optimization set, and obtains low-dimensional sparse features by using SAE dimension reduction; the customer portrait representation learning module generates a shared representation vector, on which each customer portrait task tower is set; the graph portrait output module generates a labeled portrait through dynamic optimization of reinforcement learning, and constructs a graph portrait by combining knowledge graph to complete entity relationship, the system accurately depicts the characteristics of hospital customers such as equipment failure risk, maintenance renewal willingness, spare parts demand and training demand, and provides data support for medical equipment service.