6978results about "Genetic algorithms" patented technology

A method of modelling system behaviour of a physical system, the method including, in one or more electronic processing devices obtaining quantified system data measured for the physical system, the quantified system data being at least partially indicative of the system behaviour for at least a time period, forming at least one population of model units, each model unit including model parameters and at least part of a model, the model parameters being at least partially based on the quantified system data, each model including one or more mathematical equations for modelling system behaviour, for each model unit calculating at least one solution trajectory for at least part of the at least one time period; determining a fitness value based at least in part on the at least one solution trajectory; and, selecting a combination of model units using the fitness values of each model unit, the combination of model units representing a collective model that models the system behaviour.

Disclosed are methods and apparatus for conducting market research and developing product designs. The methods involve generating and presenting, typically electronically, generations of design alternatives to persons participating in the design, selection, or market research exercise. The participants transmit data indicative of their preferences among or between the presented design alternatives. Some of the data is used to conduct a conjoint analysis or non-convergent exercise to investigate the drivers of the preferences of the group or its members, and at least a portion are used to derive follow-on generations of design alternatives or proposals. The follow-on designs are preferably generated through the use of an evolutionary or genetic computer program, influenced by the participants' preferences. The process results in the generation of one or more preferred product forms and information permitting a better understanding of what attributes of the product influence the preferences of the test group members.

Online fraud prevention including receiving a rules set to detect fraud, mapping the rules set to a data set, mapping success data to members of the rules set, filtering the members of the rules set, and ordering members of the data set by giving priority to those members of the data set with a greater probability for being fraudulent based upon the success data of each member of the rule set in detecting fraud. Further, a receiver coupled to an application server to receive a rules set to detect fraud, and a server coupled to the application server, to map the rules set to a data set, and to map the success data to each members of the rules set. The server is used to order the various members of the data set by giving priority to those members of the data set with a greatest probability for being fraudulent.

The Quanton virtual machine approximates solutions to NP-Hard problems in factorial spaces in polynomial time. The data representation and methods emulate quantum computing on classical hardware but also implement quantum computing if run on quantum hardware. The Quanton uses permutations indexed by Lehmer codes and permutation-operators to represent quantum gates and operations. A generating function embeds the indexes into a geometric object for efficient compressed representation. A nonlinear directional probability distribution is embedded to the manifold and at the tangent space to each index point is also a linear probability distribution. Simple vector operations on the distributions correspond to quantum gate operations. The Quanton provides features of quantum computing: superpositioning, quantization and entanglement surrogates. Populations of Quantons are evolved as local evolving gate operations solving problems or as solution candidates in an Estimation of Distribution algorithm. The Quanton representation and methods are fully parallel on any hardware.

Automatically characterizing a behavior of an object or objects by processing object data to obtain a data set that records a measured parameter set for each object over lime, providing a learning input that identifies when the measured parameter set of an object is associated with a behavior, processing the data set in combination with the learning input to determine which parameters of the parameter set over which range of respective values characterize the behavior; and sending information that identifies which parameters of the parameter set over which range of respective values characterize the behavior for use in a process that uses the characteristic parameters and their characteristic ranges to process second object data and automatically identify when the behavior occurs. Also disclosed is a method of tracking one or more objects.

The cost of performing sophisticated software-based financial trend and pattern analysis is significantly reduced by distributing the processing power required to carry out the analysis and computational task across a large number of networked individual or cluster of computing nodes. To achieve this, the computational task is divided into a number of sub tasks. Each sub task is then executed on one of a number of processing devices to generate a multitude of solutions. The solutions are subsequently combined to generate a result for the computational task. The individuals controlling the processing devices are compensated for use of their associated processing devices. The algorithms are optionally enabled to evolve over time. Thereafter, one or more of the evolved algorithms is selected in accordance with a predefined condition.

Optimizing and/or personalizing activities to a user through artificial intelligence and/or virtual reality.

A method includes determining a plurality of data points, determining a distance between each data point and each of the other plurality of data points, choosing a first one of the plurality of data points, identifying all of the other plurality of data points within a maximum distance of the chosen data point, repeating steps the previous steps to choose a different one of the plurality of data points until all of the data points have been chosen, identifying one or more clusters each having a predefined minimum number, K, of data points within a predefined search radius and analyzing the one or more clusters with respect to K linkages.

The invention involves generating and presenting, typically electronically, a number of design alternatives to persons who are participating in the design, selection, or market research exercise. The participants (referred to as “selectors”) transmit data indicative of their preferences among or between the presented design alternatives, and that data is used to derive a new generation of design alternatives or proposals. The new designs are generated through the use of a computer program exploiting a genetic or evolutionary computational technique. The process is repeated, typically for many iterations or cycles.

An artificial intelligence (AI) combination platform or orchestration module is described that orchestrates and automates the processes of selecting, building, testing, and delivering new or integrated computer vision, detection, sensor and perception algorithms that can be published or leveraged in near real time by sensors, hardware or applications. The AI combination platform includes one or more of the following engines: algorithm mixer engine, retrospective search engine, algorithm recommendation engine, quality assurance engine, certification engine, and perception engine.

A novel and powerful fluence and beam orientation optimization package for radiotherapy optimization, called PARETO (Pareto-Aware Radiotherapy Evolutionary Treatment Optimization), makes use of a multi-objective genetic algorithm capable of optimizing several objective functions simultaneously and mapping the structure of their trade-off surface efficiently and in detail. PARETO generates a database of Pareto non-dominated solutions and allows the graphical exploration of trade-offs between multiple planning objectives during IMRT treatment planning PARETO offers automated and truly multi-objective treatment plan optimization, which does not require any objective weights to be chosen, and therefore finds a large sample of optimized solutions defining a trade-off surface, which represents the range of compromises that are possible.

Agent-based models (ABMs)/multi-agent systems (MASs) are one of the most widely used modeling-simulation-analysis approaches for understanding the dynamical behavior of complex systems. These models can be often characterized by several parameters with nonlinear interactions which together determine the global system dynamics, usually measured by different conflicting criteria. One problem that can emerge is that of tuning the controllable system parameters at the local level, in order to reach some desirable global behavior. According to one exemplary embodiment t of the present invention, the tuning of an ABM for emergency response planning can be cast as a multi-objective optimization problem (MOOP). Further, the use of multi-objective evolutionary algorithms (MOEAs) and procedures for exploration and optimization of the resultant search space can be utilized. It is possible to employ conventional MOEAs, e.g., the Nondominated Sorting Genetic Algorithm II (NSGA-II) and the Pareto Archived Evolution Strategy (PAES), and their performance can be tested for different pairs of objectives for plan evaluation. In the experimental results, the approximate Pareto front of the non-dominated solutions is effectively obtained. Further, a conflict between the proposed objectives can be seen. Additional robustness analysis may be performed to assist policy-makers in selecting a plan according to higher-level information or criteria which is likely not present in the original problem description.

A method and apparatus is provided for constructing a predictive model for a system based on a priori qualitative modeling of the system and on historical database collected from past activity of the system or past events in the system. An expert provides grouping of parameters and qualitative dependencies between parameters and attributes, wherein some of the attributes may be conceptual or virtual attributes. The present invention extends existing methods of `evolutionary algorithms` in order to build successive sets of quantitative predictive models for the system, wherein parts of each model are evolved by the evolutionary algorithm and parts of each model are derived using the historical database. According to the present invention a model constructed by this method can be incorporated as a predictive model into a diagnosis or control apparatus without the need for human inspection, as the model complies with the expert's knowledge about the system. The present invention also provides a method to update the constructed model when new data is delivered, thus adjusting the model to changes in the environment.

A method includes: a) producing initial scheduling plans based on requests related to tasks to be performed within a time period by a remote sensing satellite; wherein each initial scheduling plan schedules respective tasks, which do not conflict with each other in time and in using satellite resources; and each task is scheduled in at least one of the initial scheduling plans; b) applying a genetic-algorithm-based processing to the initial scheduling plans to produce a genetic-algorithm-based scheduling plan which is for mission objectives, and complies with constraints related to the satellite resources, to the tasks, and to the time period; and c) applying a simulated-annealing-based processing to the genetic-algorithm-based scheduling plan to produce a simulated-annealing-based scheduling plan that fits the mission objectives, complies with the constraints, and in which a larger number of tasks is scheduled than in the genetic-algorithm-based scheduling plan.

An embodiment of a method for optimizing a solution set has steps of generating a first solution set, selecting a second solution set from the first, fitting the second solution set with a probabilistic model, using the model to generate a new set of solutions, replacing at least a portion of the first set of solutions with the third, and evaluating the third set to determine if completion criteria have been met. A probabilistic model may allow for merging a plurality of variables into a single variable and for modeling relationships between the merged variables over multiple hierarchical levels. Invention method embodiments may also comprise steps of niching to preserve diversity among the solution set.

A system for generation of business executables using artificial intelligence or rules distributed among multiple hardware devices, including: a memory element of a specially programmed general-purpose computer storing a first rule or an artificial intelligence (AI) program; a generating element, in a processor for the specially programmed computer, arranged to generate at an executable using at least one of the first rule or the AI program; an interface element of the specially programmed computer, arranged to receive a second rule from a first wireless communications device (WCD), or from a general-purpose computer associated with a business entity and to store the second rule in the memory element; and a modifying element, in the processor, arranged to modify the executable using the second rule and to transmit, using the interface element, the modified executable to a wireless communications network for transmission to a second WCD.