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公开(公告)号：US20240311441A1
公开(公告)日：2024-09-19
申请号：US18603744
申请日：2024-03-13
Inventor： Babak Hodjat , Hormoz Shahrzad , Risto Miikkulainen
IPC: G06F17/11
CPC classification number: G06F17/11
Abstract: A domain-independent problem-solving system and process addresses domain-specific problems with varying dimensionality and complexity, solving different problems with little or no hyperparameter tuning, and adapting to changes in the domain, thus implementing lifelong learning.
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公开(公告)号：US11574202B1
公开(公告)日：2023-02-07
申请号：US16590209
申请日：2019-10-01
Inventor： Hormoz Shahrzad , Babak Hodjat , Risto Miikkulainen
IPC: G06N3/12
Abstract: Roughly described, an evolutionary data mining system includes at least two processing units, each having a pool of candidate individuals in which each candidate individual has a fitness estimate and experience level. A first processing unit tests candidate individuals against training data, updates an individual's experience level, and assigns each candidate to one of multiple layers of the candidate pool based on the individual's experience level. Individuals within the same layer of the same pool compete with each other to remain candidates. The first processing unit selects a set of candidates to retain based on the relative novelty of their responses to the training data. The first processing unit reports successful individuals to the second processing unit, and receives individuals for further testing from the second processing unit. The second processing unit selects individuals to retain based on their fitness estimate.
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公开(公告)号：US20220207362A1
公开(公告)日：2022-06-30
申请号：US17554097
申请日：2021-12-17
Inventor： Elliot Meyerson , Risto Miikkulainen
Abstract: A general prediction model is based on an observer traveling around a continuous space, measuring values at some locations, and predicting them at others. The observer is completely agnostic about any particular task being solved; it cares only about measurement locations and their values. A machine learning framework in which seemingly unrelated tasks can be solved by a single model is proposed, whereby input and output variables are embedded into a shared space. The approach is shown to (1) recover intuitive locations of variables in space and time, (2) exploit regularities across related datasets with completely disjoint input and output spaces, and (3) exploit regularities across seemingly unrelated tasks, outperforming task-specific single-task models and multi-task learning alternatives.
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公开(公告)号：US20220109654A1
公开(公告)日：2022-04-07
申请号：US17064706
申请日：2020-10-07
Inventor： Daniel E. Fink , Jason Liang , Risto Miikkulainen
Abstract: Systems and processes for facilitating the sharing of models trained on a data set confined within a given firewall, i.e., a hidden data set, along with the model's performance metrics are described. The trained models may be used in further processes to improve the trained models to solve a predetermined problem or make a prediction.
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公开(公告)号：US20220013241A1
公开(公告)日：2022-01-13
申请号：US17355971
申请日：2021-06-23
Inventor： Elliot Meyerson , Olivier Francon
Abstract: The present invention relates to an ESP decision optimization system for epidemiological modeling. ESP based modeling approach is used to predict how non-pharmaceutical interventions (NPIs) affect a given pandemic, and then automatically discover effective NPI strategies as control measures. The ESP decision optimization system comprises of a data-driven predictor, a supervised machine learning model, trained with historical data on how given actions in given contexts led to specific outcomes. The Predictor is then used as a surrogate in order to evolve prescriptor, i.e. neural networks that implement decision policies (i.e. NPIs) resulting in best possible outcomes. Using the data-driven LSTM model as the Predictor, a Prescriptor is evolved in a multi-objective setting to minimize the pandemic impact.
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Patent Agency Ranking
公开(公告)号：US20200351290A1
公开(公告)日：2020-11-05
申请号：US16934935
申请日：2020-07-21
Inventor： Babak Hodjat
Abstract: Roughly described, anomalous behavior of a machine-learned computer-implemented individual can be detected while operating in a production environment. A population of individuals is represented in a computer storage medium, each individual identifying actions to assert in dependence upon input data. As part of machine learning, the individuals are tested against samples of training data and the actions they assert are recorded in a behavior repository. The behavior of an individual is characterized from the observations recorded during training. In a production environment, the individuals are operated by applying production input data, and the production behavior of the individual is observed and compared to the behavior of the individual represented in the behavior repository. A determination is made from the comparison of whether the individual's production behavior during operation is anomalous.
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公开(公告)号：US20200346073A1
公开(公告)日：2020-11-05
申请号：US16934681
申请日：2020-07-21
Inventor： Elliot Meyerson , Risto Miikkulainen
Abstract: Roughly described, a computer system uses a behavior-driven algorithm that is better able to find optimum solutions to a problem by balancing the use of fitness and novelty measures in evolutionary optimization. In competition among candidate individuals, a domination estimate between a pair of individuals is determined by both their fitness estimate difference and their behavior difference relative to one another. An increase in the fitness estimate difference of one individual of the pair over the other increases the domination estimate of the first individual. An increase in the behavior distance between the pair of individuals decreases the domination estimate of both of the individuals. Individuals with a higher domination estimate are more likely to survive competitions among the candidate individuals.
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公开(公告)号：US20200012648A1
公开(公告)日：2020-01-09
申请号：US16502439
申请日：2019-07-03
Inventor： Daniel E. Fink
IPC: G06F16/2455 , G06F16/22
Abstract: A system and process for generalizing an evolutionary process applied to a particular domain involving different problems includes a researcher module for generating a configuration specification applicable to a particular problem. An evolution module parses the configuration specification into a representative tree structure, assembles policies for each node in the tree structure, and generates candidate genomes using the policies for each node in the tree structure. The policies may be applied to new data or data from prior runs to generate candidate genomes. The evolution module translates internal representations of the generated candidate genomes into known representations of the candidate genome for evaluation in accordance with the particular domain parameters by a candidate evaluation module.
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公开(公告)号：US20190180188A1
公开(公告)日：2019-06-13
申请号：US16219286
申请日：2018-12-13
Inventor： Jason Zhi Liang , Elliot Meyerson , Risto Miikkulainen
Abstract: Evolution and coevolution of neural networks via multitask learning is described. The foundation is (1) the original soft ordering, which uses a fixed architecture for the modules and a fixed routing (i.e. network topology) that is shared among all tasks. This architecture is then extended in two ways with CoDeepNEAT: (2) by coevolving the module architectures (CM), and (3) by coevolving both the module architectures and a single shared routing for all tasks using (CMSR). An alternative evolutionary process (4) keeps the module architecture fixed, but evolves a separate routing for each task during training (CTR). Finally, approaches (2) and (4) are combined into (5), where both modules and task routing are coevolved (CMTR).
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公开(公告)号：US11995559B2
公开(公告)日：2024-05-28
申请号：US17702632
申请日：2022-03-23
Inventor： Xin Qiu , Risto Miikkulainen
CPC classification number: G06N3/126
Abstract: A computer-implemented method optimizing genetic algorithms for finding solutions to a provided problem is described. The method implements a multi-arm bandit algorithm to determine performance scores for candidate individuals from a candidate pool in dependence on successes and failures of the one or more candidates. The method evolves the candidate individuals in the candidate pool by performing evolution steps including: determining a fitness score for each of the candidate individuals in the candidate pool in dependence on the performance scores for the candidate individuals, discarding candidate individuals from the candidate pool in dependence upon their assigned performance measure, and adding, to the candidate pool, a new candidate individual procreated from candidate individuals remaining in the candidate pool after the discarding of the candidate individuals. This evolution is repeated evolve the candidate individuals in the candidate pool and one or more candidate individuals from the candidate pool is selected based on best neighborhood performance measures, wherein the selected winning candidate individual is a solution to the provided problem.
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