Systems, methods, and apparatuses for generating and using machine learning models using genetic data. A set of input features for training the machine learning model can be identified and used to train the model based on training samples, e.g., for which one or more labels are known. As examples, the input features can include aligned variables (e.g., derived from sequences aligned to a population level or individual references) and/or non-aligned variables (e.g., sequence content). The features can be classified into different groups based on the underlying genetic data or intermediate values resulting from a processing of the underlying genetic data. Features can be selected from a feature space for creating a feature vector for training a model. The selection and creation of feature vectors can be performed iteratively to train many models as part of a search for optimal features and an optimal model.

one embodiment of a memory stores information, including address bits, on DNA strands and provides access using a pipeline of tubes, where each tube selectively transfers half of the strands to the next tube based on probing of associated address bits. Transfers are controlled by logic relating to the state of the tubes: The pipeline may be initialized to start at a high-order target address, providing random access without enzymes, synthesizing probe molecules or PCR at access time. Thereafter, a processing unit gets fast access to sequentially addressed strands each cycle, for applications like executing machine language instructions or reading blocks of data from a file. Another embodiment with a compare unit allows low-order random access. Provided that addresses are encoded using single-stranded regions of DNA where probe molecules may hybridize, other information may use any DNA encoding. Electronic/electrochemical (electrowetting, nanopore, etc.) embodiments as well as biochemical embodiments are possible.

An intelligent ammunition co-evolution task assignment method is disclosed. The method includes the following steps. A chromosome gene encoding of genetic algorithm for ammunition assignment scheme of multi-platform interception operation is performed. A fitness of chromosome individual function in a genetic population is calculated according to a threat degree of an intercepting target in the ammunition assignment scheme and an interception probability of the intercepting target by different ammunition launching platforms. A probability ranking sequence of effective interception of the intercepting target by different ammunition launching platforms is obtained, and a genetic algorithm selection operation is performed on the chromosome individuals according to probability values of effective interception in descending order of priority. Crossover and/or mutation are performed on the selected chromosome individuals to obtain a next generation genetic population and the above steps are repeated until termination conditions are met to obtain the final ammunition assignment scheme.

Gene expression programming-based behavior monitoring is disclosed. A machine receives, as input, a plurality of data examples. A method can include receiving data indicating behaviors of the device, determining, using a gene expression programming (GEP) method, a data model that explains the data, and comparing further data indicating further behavior of the device to the data model to determine whether the further behavior is explained by the data model.

This disclosure relates generally to system and method for optimization of industrial processes, for example a tundish process. Typically geometries for industrial processes are simulated in a numerical analysis model such as a CFD. In order to simulate a physical phenomenon (such as tundish process) numerically, the domain thereof is discretized in order to convert the differential equations to be solved in the domain into linear equations. The accuracy of a CFD solution is dependent on a mesh of the domain, which in turn depends on a geometry thereof. For setting up an optimization task, the disclosed method provides first a CFD friendly base geometry, so that a faulty geometry can be detected before forming the complete geometry.

Methods and systems that provide one or more recommended configurations to planners using large data sets in an efficient manner. These methods and systems provide optimization of objectives using a genetic algorithm that can provide parameter recommendations that optimize one or more objectives in an efficient and timely manner. The methods and systems disclosed herein are flexible enough to satisfy diverse use cases.

A method for training a machine learning model includes obtaining camouflage material data. The method includes obtaining environmental data. The method also includes generating the machine learning model based on the camouflage material data and the environmental data. The method includes generating a plurality of camouflage patterns based on the machine learning model. The method includes assigning a rank to each of the camouflage patterns. The method further includes training the machine learning model with a camouflage pattern assigned with a highest rank.

Computer-implemented methods of training machine learning models and using the machine learning models for tailoring a multi-channel help desk environment. One or more computers train a machine learning model of selecting best attendance channels for respective customer clusters and for respective issue clusters. One or more computers train machine learning models of tailoring respective attendance channel types. One or more computers employ the machine learning models to determine a best attendance channel for resolving an information technology problem of a user and to predict channel tailoring characteristics for the best attendance channel. One or more computers employ genetic algorithm operators to determine a random attendance channel with random tailoring characteristics. One or more computer use random routing to route the user to one of the best attendance channel and the random attendance channel, avoiding undesired bias favorable toward the best attendance channel.

Computer-implemented methods of training machine learning models and using the machine learning models for tailoring a multi-channel help desk environment. One or more computers train a machine learning model of selecting best attendance channels for respective customer clusters and for respective issue clusters. One or more computers train machine learning models of tailoring respective attendance channel types. One or more computers employ the machine learning models to determine a best attendance channel for resolving an information technology problem of a user and to predict channel tailoring characteristics for the best attendance channel. One or more computers employ genetic algorithm operators to determine a random attendance channel with random tailoring characteristics. One or more computer use random routing to route the user to one of the best attendance channel and the random attendance channel, avoiding undesired bias favorable toward the best attendance channel.

An information processing apparatus includes: a soma-related information storage unit in which two or more pieces of soma-related information having a soma identifier are stored; a connection information storage unit in which one or more pieces of connection information for specifying connection between two or more somas are stored; an information transfer unit that acquires soma identifiers of one or more somas that accept information based on accepted input information; an output information acquiring unit that acquires output information, which is information that is output, using the information accepted by each soma identified with the one or more soma identifiers acquired by the information transfer unit; an information output unit that outputs the output information; and a growth unit that performs soma generation processing for generating soma-related information and accumulating the information in the soma-related information storage unit.