A genetic algorithm system generates a set of computer programs and executes a process for assessment and conditional modification of the set, repeating the process over a plurality of generations to mutate the population of solutions over time. At each generation, the system scores each program in the set to generate a respective primary score adjustment, a respective secondary score adjustment, and a respective current score. If a current score for a program is less than or equal to a first threshold, the system removes the computer program from the set. If the current score is greater than or equal to a second threshold, the system modifies the computer program to generate one or more offspring programs for use in subsequent generations. If a primary score adjustment for a program is greater than or equal to a third threshold, the system selects the computer program for performance of a task.

Neural networks can be implemented with DNA strand displacement (DSD) circuits. The neural networks are designed and trained in silico taking into account the behavior of DSD circuits. Oligonucleotides comprising DSD circuits are synthesized and combined to form a neural network. In an implementation, the neural network may be a binary neural network in which the output from each neuron is a binary value and the weight of each neuron either maintains the incoming binary value or flips the binary value. Inputs to the neural network are one more oligonucleotides such as synthetic oligonucleotides containing digital data or natural oligonucleotides such as mRNA. Outputs from the neural networks may be oligonucleotides that are read by directly sequencing or oligonucleotides that generate signals such as by release of fluorescent reporters.

A method for processing genetic data, which comprise a series of sequence elements each representing a biomolecule, comprises the steps of forming sequence fragments (S2), wherein each sequence fragment comprises a section of the series of sequence elements having a fragment length of at least two sequence elements, applying a coding function to each of the sequence fragments in order to generate a multiplicity of encrypted fragment data items (S3) winch are each assigned to one of the sequence fragments, and storing the encrypted fragment data (S4), wherein the sequence fragments are formed in such a manner that the sections of the series of sequence elements overlap and each sequence element is included in at least two sequence fragments. A description is also given of a data processing device for processing genetic data and a method for querying a database containing encrypted fragment data which were generated and stored using the method for processing genetic data.

The present disclosure relates to an authentication device using DNA base sequence information, the authentication device including an authentication means, in which a plurality of DNA base sequence information are included in authentication information derived by reading out the authentication means composed of a plurality DNAs.

The present invention discloses a detection method, a system, an electronic equipment, and a storage medium of product test data, where the detection method includes: obtaining historical test data of historical batches of products; screening the historical test data to obtain intermediate test data; grouping the intermediate test data based on preset test parameters to obtain first groups; obtaining distribution patterns of the first groups based on the intermediate test data of the first groups; when the distribution pattern is a preset distribution pattern, using the first group corresponding to the distribution pattern as a target group; and obtaining a target test limit value based on the intermediate test data corresponding to the target group. In the present invention, the test limit value can be adjusted dynamically and adaptively, and chip test data with abnormal data can be effectively detected in real time, which improves test quality of the chip.

The present disclosure describes a system and method for improving the way computing devices execute genetic algorithms. A fuzzy logic controller takes various properties of the genetic algorithm (such as the diversity of the population, the performance history of the algorithm in terms of time-efficiency and/or effectiveness at improving the best fitness function results, and available computing resources) to dynamically manage the parameters of the genetic algorithm. In some embodiments, the fuzzy inference system that provides parameters to the genetic algorithm is itself controlled by another fuzzy inference system.

The present disclosure describes a system and method for improving the way computing devices execute genetic algorithms. A fuzzy logic controller takes various properties of the genetic algorithm (such as the diversity of the population, the performance history of the algorithm in terms of time-efficiency and/or effectiveness at improving the best fitness function results, and available computing resources) to dynamically manage the parameters of the genetic algorithm. In some embodiments, the fuzzy inference system that provides parameters to the genetic algorithm is itself controlled by another fuzzy inference system.

A method includes: generating a plurality of individuals of a current generation in accordance with a plurality of individuals of a previous generation to acquire values of an objective function for individuals each representing a variable by evolutionary computation; calculating, for each of partial individuals of the plurality of individuals of the current generation generated by the generating processing, a first value of the objective function by a predetermined method; approximately calculating, for each of the plurality of individuals of the current generation, a second value of the objective function with lower precision than the predetermined method; computing a fitness difference representing a difference between the plurality of individuals of the current generation in accordance with the first value or the second value; and controlling the precision of the approximate calculation based on the fitness difference and a precision difference between the first value and the second value.

The subject technology is related to methods and apparatus for training a set of regression machine learning models with a training set to produce a set of predictive values for a pending manufacturing request, the training set including data extracted from a set of manufacturing transactions submitted by a set of entities of a supply chain. A multi-objective optimization model is implemented to (1) receive an input including the set of predictive values and a set of features of a physical object, and (2) generate an output with a set of attributes associated with a manufacture of the physical object in response to receiving the input, the output complying with a multi-objective condition satisfied in the multi-objective optimization model.

A machine learning system and method for optimizing a production process. For instance, the method includes several steps as follows: selecting different values for a plurality of input parameters of a digital model of the production process for simulation; running the digital model using the different values for the plurality of input parameters and at least some of real-time data of the production process; determining a plurality of output parameters of the digital model; analyzing the plurality of output parameters; learning an optimized plurality of input parameters corresponding to the plurality of output parameters; and programming the production process to use the optimized plurality of input parameters to run the production process.