89 results about "Genetic programming" patented technology

In a computer implemented learning and / or process control system, a computer model is constituted by the most currently fit entity in a population of computer program entities. The computer model defines fitness as a function of inputs and outputs. A computing unit accesses the model with a set of inputs, and determines a set of outputs for which the fitness is highest. This associates a sensory-motor (input-output) state with a fitness in a manner that might be termed "feeling".The learning and / or control system preferably utilizes a Compiling Genetic Programming System (CGPS) in which one or more machine code entities such as functions are created which represent solutions to a problem and are directly executable by a computer. The programs are created and altered by a program in a higher level language such as "C" which is not directly executable, but requires translation into executable machine code through compilation, interpretation, translation, etc. The entities are initially created as an integer array that can be altered by the program as data, and are executed by the program by recasting a pointer to the array as a function type. The entities are evaluated by executing them with training data as inputs, and calculating fitnesses based on a predetermined criterion. The entities are then altered based on their fitnesses using a genetic machine learning algorithm by recasting the pointer to the array as a data (e.g. integer) type. This process is iteratively repeated until an end criterion is reached.

A system of self-organizing mobile robotic agents (MRAs) in a multi-robotic system (MRS) is disclosed. MRAs cooperate, learn and interact with the environment. The system uses various AI technologies including genetic algorithms, genetic programming and evolving artificial neural networks to develop emergent dynamic behaviors. The collective behaviors of autonomous intelligent robotic agents are applied to numerous applications. The system uses hybrid control architectures. The system also develops dynamic coalitions of groups of autonomous MRAs for formation and reformation in order to perform complex task.

A method utilizing genetic programming to construct history matching and forecasting proxies for reservoir simulators. Acting as surrogates for computer simulators, the genetic programming proxies evaluate a large number of reservoir models and predict future production forecasts for petroleum reservoirs.

A method utilizing genetic programming to construct history matching and forecasting proxies for reservoir simulators. Acting as surrogates for computer simulators, the genetic programming proxies evaluate a large number of reservoir models and predict future production forecasts for petroleum reservoirs.

A provably correct computer program can be generated using genetic programming techniques. A desired behavior is used to define a formal specification. An initial population of programs is created where each program has a meaning that can be defined using a formalization technique. A fitness function is applied to measure a distance between the meaning of a program, i.e., its actual behavior, and the specification. Any program having a zero value as the measure of distance between the meaning of the program and the specification is determined to be provably correct. After the fitness of some or all of the programs in the current generation of programs has been computed, a provably correct program has not yet been found in the current generation, mutation and / or crossover techniques are performed on at least some of the fittest individuals in the current generation to create the programs of a next generation.

The invention discloses an image retrieval method based on hierarchical features and genetic programming relevance feedback. The method includes the steps: (1), performing adaptive segmentation for a retrieval image submitted by a user to obtain segmented regions; (2) extracting global features of the retrieval image, and extracting local low-level features of the segmented regions; (3) computing an optimal region, corresponding to each segmented region, of each image in a standard image library; (4) constructing a global-optimal region similarity matched pattern; (5) by attaching average weight to various similarities in the similarity matched pattern, computing similarity of the retrieval image to each image in the standard image library, sorting according to the similarity to obtain an initial retrieval result, and returning a plurality of previous images most similar to the retrieval image of the user to a user side; and (6) enabling the user to participate in feedback until satisfactory images are retrieved. The method can be closer to retrieval intention of the user, and is capable of extracting content features of the image effectively and completing retrieval quickly and effectively.

The invention relates to compositions and methods for the in vivo production of polypeptides comprising one or more unnatural amino acids. Specifically, the invention provides plasmid systems for the efficient eubacterial expression of polypeptides comprising one or more unnatural amino acids at genetically-programmed positions.

An XML-based symbolic computer language, interpreter, and corresponding execution environments are disclosed. The XML-based symbolic computer language, called “OLIN” (One Language Intelligent Network) enables a computer program to be written as an XML-compliant document. OLIN programs are interpreted by an interpreter by parsing the XML content to extract symbolic expressions embedded therein, and evaluating those symbolic expressions. The XML-based symbolic computer language is object-oriented and is based on inherent principles of the LISP programming language. Accordingly, code and data are treated the same. The language provides built-in structures and functions for implementing neural networks and genetic programming. OLIN programs may be executed by a single computer, or via a multiprocessing environment, including distributing processing environments and multiprocessor computers.

The invention discloses a genetic programming-based analog circuit fault test optimal sequential search method. A plurality of fault diagnosis trees are firstly randomly generated according to a test dependency matrix of a modular circuit, a genetic programming method is adopted to carry out selection, crossover and mutation on the fault diagnosis trees, a reciprocal of the test cost corresponding to the fault diagnosis tree serves as the individual fitness of the fault diagnosis tree, a fault rally point and a sub tree are exchanged only during the crossover process according to features of the fault diagnosis tree, a testing point is only selected for mutation during the mutation process, a sub tree under the testing point and the rally point is generated again, the fault diagnosis tree with the minimal test cost is selected from the current plurality of fault diagnosis trees after multiple times of iteration to serve as the final fault diagnosis tree. Through accurate search, a testing point sequence isolating the maximal fault points at the minimal test cost can be obtained, and guidance is provided for modular circuit fault test.

A method, system, and device for reducing connection time to a wireless access point includes recording wireless connection information in a log, remotely computing parameters as a function of past wireless connection information in the log, generating an ordered list of wireless access points most likely to be available for reconnection at a desired time as a function of recent wireless connection information in the log, and directly probing a wireless access point instead of initiating a wireless access point scan. In some embodiments, remotely computing parameters as a function of past wireless connection information in the log comprises performing genetic programming operations to generate prediction programs for later prediction of wireless access points most likely to be available for reconnection at a desired time.

A provably correct computer program can be generated using genetic programming techniques. A desired behavior is used to define a formal specification. An initial population of programs is created where each program has a meaning that can be defined using a formalization technique. A fitness function is applied to measure a distance between the meaning of a program, i.e., its actual behavior, and the specification. Any program having a zero value as the measure of distance between the meaning of the program and the specification is determined to be provably correct. After the fitness of some or all of the programs in the current generation of programs has been computed, a provably correct program has not yet been found in the current generation, mutation and / or crossover techniques are performed on at least some of the fittest individuals in the current generation to create the programs of a next generation.

A fault tolerant software environment, in which various program components (e.g., portions of computer programs, applications, etc) are objectized into entities represented by “codons.” This allows for improper syntax to occur, enabling, for example, combinatorial operations such as genetic programming. The present invention also contemplates such features as the ability to probabilistically execute individual codons, to switch between treating information as executable code or as data (or passing over it), provides that the individual codons can be tagged so that additional information can be associated with them, and provides for tagging of the stack.

The invention discloses a method for cement hydration simulation and macroscopic attribute predication based on microcosmic image characteristics. At first, a three-dimensional micro computer fault scanner is adopted to obtain evolution of a cement microstructure, and macroscopic attributes of the cement microstructure are measured at the same time; then, three-dimensional image characteristics are adopted to describe microstructures of cement on each age, and a Genetic programming type algorithm is utilized to directly set up a dynamic relation described in a power system mode between the image characteristics and the macroscopic attributes; the obtained power system is used for simulating cement hydration and predicating the macroscopic attributes. According to the method, complicated image segmentation and phase classification for microcosmic images of the cement are avoided, physical and chemical knowledge does not need to be considered, the three-dimensional image characteristics are calculated from images themselves, the dynamic relation between the image characteristics and the macroscopic attributes is further obtained, influences brought by incorrect phase analysis and image segmentation are avoided, and thus prediction accuracy for development and variation tendencies of the macroscopic attributes is improved.