12 results about "Genetic programming" patented technology

The invention discloses a hyper-heuristic order grouping and robot sorting path integrated planning method based on genetic programming. The method comprises the following steps: acquiring order demand information and warehouse layout information of an intelligent manufacturing workshop production system; determining an objective function of order grouping and sorting path planning; establishing a double-commodity network flow model integrating order grouping and path planning; setting constraint conditions for the double-commodity network flow model; a priority function rule is generated in a genetic programming hyper-heuristic mode, a dynamic programming strategy is combined, the double-commodity network flow model is solved, and the optimal result of order grouping and batching and robot sorting paths is obtained. According to the method, the sorting efficiency optimization problem of multi-variety and high-frequency orders in the intelligent manufacturing feeding warehouse is solved, and the solving efficiency and rule generalization ability can be remarkably improved, so that the sorting time is effectively shortened, the intelligent manufacturing short window requirement is met, and theoretical guidance and practical support are provided for improvement of the warehousing feeding efficiency.

The invention belongs to the technical field of intelligent manufacturing, and discloses a digital twinning workshop real-time scheduling method for limited transportation resources and charging constraint scenes, which comprises the following steps: constructing a real-time scheduling framework based on digital twinning and deep reinforcement learning to realize real-time interaction of virtual and real data; proposing a two-stage real-time scheduling model based on deep reinforcement learning; taking the minimum completion time as a target, and establishing a Markov decision process of the DFJSP-LTR-C; five key elements including an interaction point, a real-time scheduling process, a real-time state feature, an action space based on improved genetic programming and a composite reward function are designed for the scheduling model; an IAD3QN training method based on a multi-head attention mechanism is provided to train a scheduling agent. According to the method, adaptive collaborative optimization of production and transportation resources is realized under the condition of considering limited transportation resources and charging constraints, the maximum completion time is effectively reduced, and the feasibility and robustness of a scheduling scheme in an actual workshop are improved.

The invention discloses a dual-tree genetic programming method for processing a long-tail image classification problem, and the method comprises the following steps: constructing a dual-tree genetic programming individual which comprises a data enhancement tree and a feature extraction tree; setting a staged sampler; performing a multi-generation evolution process, wherein each generation comprises population initialization, individual fitness evaluation, selection, crossover and mutation operation; after the evolution is finished, outputting a dual-tree individual with the highest fitness as a final classification model for classifying a new image; the method is simple and efficient in model deployment and has practical value.

This invention relates to the fields of artificial intelligence and computer vision, and provides a few-shot fine-grained image classification method based on multi-scale feature extraction, attention, and relation enhancement using genetic programming. The method includes: extracting multi-scale deep features using a pre-trained deep feature extraction network (preferably ResNet-18); designing a strongly typed genetic programming tree structure comprising an input layer, a multi-scale feature extraction layer, a channel selection layer, an attention layer, a feature concatenation layer, and an output layer; enhancing discriminative regions and suppressing background noise through a spatial attention mechanism; developing a prototype-query relation enhancement module to amplify the regions of common interest between the support set and the query set to enhance feature representation; and using the sum of classification accuracy and cluster separation score as a fitness function to guide the evolutionary search. This invention combines deep neural network representation with genetic programming evolutionary search, achieving excellent classification performance on fine-grained few-shot image classification tasks while maintaining interpretability.

The application discloses a photovoltaic power station power prediction method based on prior knowledge guided genetic programming, first, obtaining training data preprocessed by symbol normalization, learning photovoltaic power generation symbol rules through self-supervised pre-training of a Transformer model, and outputting a prior symbol vector of a target function; a double fitness collaborative optimization mechanism containing numerical fitting accuracy and physical mechanism similarity is designed, and the prior vector is used to strengthen and select individuals with high physical matching degree; a Pareto multi-objective optimization is used to balance the double-dimensional conflict, the prior knowledge is transferred to the whole evolution process of genetic programming, and finally the optimal explainable prediction model is extracted from the Pareto frontier solution set. The application solves the problems of traditional genetic programming evolution blindness, easy falling into local optimum and poor physical consistency of generated model, realizes explicit physical explainability of the model while ensuring prediction accuracy, and can be widely applied to power generation scheduling and operation optimization of photovoltaic power stations.

This invention discloses a method and system for constructing a rainfall-runoff model. The method involves collecting and preprocessing relevant daily sequence data within a watershed; constructing a hydrological model component library; pre-assembling the hydrological model structure; defining a parameter space; performing a collaborative search using genetic programming; simulating daily runoff using the optimal model structure and parameters obtained through the search and calibration; evaluating the model's generalization performance; and outputting the optimal model structure type, optimized parameter vector, calibration set, validation set performance indicators, and runoff simulation sequence. This invention can automatically generate rainfall-runoff models with adaptive model structures and interpretable hydrophysical mechanisms, effectively improving the automatic discovery capability of model structures and the interpretability of hydrophysical mechanisms, while reducing manual workload while avoiding overfitting.

The application discloses an unmanned aerial vehicle (UAV) inspection path planning method based on automatic target differential evolution, which comprises the following steps: standardizing modeling of a UAV inspection area, setting a plurality of intermediate control points between adjacent inspection points, encoding the UAV inspection path, and constructing a fitness function; generating an initial seed solution according to the spatial distribution characteristics of the inspection points, and generating an initial population by using the initial seed solution; one individual in the initial population represents a UAV inspection path; a target vector generation operator is obtained through genetic programming offline training; the initial population is updated by target differential evolution according to the target vector generation operator to obtain a test individual; the test individual is repaired, and the optimal individual is selected according to the fitness function to enter the next generation population iteration; and the planning result is output when the termination condition is reached. The method improves the search ability and convergence efficiency of the high-dimensional path coding problem, and greatly optimizes the UAV inspection path.

The application discloses a kind of multi-objective synthetic oversampling methods for unbalanced data, based on genetic programming, the synthesis of minority class sample is modeled as constraint multi-objective optimization problem, including: genetic programming driven batch synthesis: individual is coded as a complete sample synthesis rule, a batch of high-quality samples is automatically generated in single run by evolution search, capture complex nonlinear relationship beyond linear interpolation;Constraint multi-objective evaluation mechanism: the sample generation process is modeled as constraint multi-objective optimization problem, while optimizing inter-class separation and local neighborhood structure, and from classification boundary, distribution consistency, sample diversity, sample quality four aspects explicitly constraint sample, ensure the effectiveness of generated sample;Constraint processing mechanism: design special constraint processing strategy to select feasible solution to participate in sample generation, promote population feasibility and realize batch synthesis of high-quality minority class sample;The application significantly improves sample generation efficiency, is advantageous to process large-scale unbalanced data.

This invention discloses a battery life prediction method based on pre-trained model-guided genetic programming, comprising: defining a battery life prediction target and battery cycling characteristics; generating a set of data point pairs using a mathematical expression containing battery cycling characteristics, constructing a training dataset, and pre-training a Transformer model; collecting battery data and constructing a battery dataset; inputting the battery dataset into the Transformer model, using the Transformer model to generate a guide subpopulation, and combining it with a randomly generated subpopulation to construct a hybrid initial population for genetic programming; iteratively evolving the hybrid initial population and performing mutation operations, the mutation operations being executed based on semantic backpropagation and semantic guidance of the Transformer model; determining whether the evolution has reached the termination condition, if the evolution terminates, outputting a mathematical expression, inputting the battery cycling characteristics of the battery to be tested into the mathematical expression, and obtaining the battery life prediction result.

This invention discloses a genetic programming mutation probability optimization method based on the maximum mutual information coefficient. In traditional genetic programming, feature selection tends to shift from initial completely random selection to selective selection with bias. This invention constrains the search direction of genetic programming, thereby improving search efficiency. The method includes: Step S1, using the maximum mutual information coefficient to measure the correlation between each feature and the target in the dataset, and merging the correlations of each feature and the target into a correlation vector; Step S2, determining the probability distribution of genetic programming when selecting new features through mutation based on the correlation vector; Step S3, performing genetic programming evolution, during which the probability distribution remains fixed and is unaffected by the number of features selected within the population. This invention reduces the impact of random initialization on the overall performance of genetic programming, enhances the robustness of genetic programming on fundamental problems, improves training efficiency and accuracy, and enhances model generalization performance.

Techniques and solutions are provided for enhancing software security through autonomous adaptive code evolution. Code variants are generated and analyzed using various methods, such as static code analysis or execution in controlled environments, against known and predictive future threats or vulnerabilities to determine whether they exhibit improved security. Variants can be generated using techniques such as genetic programming, instruction substitution, control flow alteration, or dead code insertion. Types of code modifications that result in improved security are prioritized when generating variants. In one example, reinforcement learning is used to identify code adaptations that enhance security, including those which do so without overly compromising functionality or performance. Continuous performance monitoring can be used to help ensure that security adaptations do not degrade software functionality, and an intelligent rollback mechanism can be used to revert to a previous state if negative impacts are detected.

The application discloses a kind of based on artificial intelligence's charging pile remote monitoring early warning method, it is related to artificial intelligence charging relevant technical field, including deployment bionic sensor network and computer vision monitoring unit, real-time monitoring is carried out to charging pile, data pre-processing, analysis is carried out using group intelligence algorithm and division cooperation submodel, judge whether there is fault and determine fault type, fault diagnosis result output and decision, start genetic programming evolution's maintenance strategy module, generate initial maintenance strategy.The application also discloses a kind of based on artificial intelligence's charging pile remote monitoring early warning system, including bionic sensor network, computer vision monitoring unit, data acquisition and transmission module, central processing system, early warning unit, control unit, man-machine interface.The application is monitored to charging pile multidimensionally by deployment bionic sensor network, comprehensively understands the state of charging pile, improves the accuracy of fault detection.