Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 35 for “"Stochastic Search"”.
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Stochastic Search and Fuzzy Modelling for Real World Complex Systems
… systems is an area of ongoing interest for the research community. Real-world systems present a variety of challenges not least of which is the problem of uncertainty inherent in their operation. In this research the problem of inventory management was chosen, the goal was to discover whether …
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Bayesian analysis of multivariate stochastic volatility and dynamic models
… is of unknown nature, may be deterministic or stochastic. We propose Bayesian stochastic search as a feasible variable selection technique for the regression and volatility equations. We develop Markov Chain Monte Carlo (MCMC) algorithms that generate a posteriori restrictions on the elements …
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New model-based methods for non-differentiable optimization
… optimization, discrete gradient-based adaptive stochastic search (discrete-GASS) and annealing gradient-based adaptive stochastic search (annealing-GASS), under the framework of gradient-based adaptive stochastic search (GASS), where the parameter of the probabilistic model is updated based on a …
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Faster Evolutionary Multi-Objective Optimization via GALE, the Geometric Active Learner
… thousands of papers that discuss methods for the search of optimal solutions to complex problems. In the case of multi-objective optimization, such a search yields iteratively improved approximations to the Pareto frontier, i.e. the set of best solutions contained along a trade-off curve of …
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Some Aspects on Data Modelling
… data sequence are commonly found in many research areas, such as finance, bioinformatics and text mining. In this dissertation, two problems regarding these two types of data: association rule mining from transaction data and structural change estimation in time-ordered sequence, are …
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Generalized hill climbing algorithms for discrete optimization problems
… include simulated annealing (SA), local search, and threshold accepting (T A), among. others. A proof of convergence of GHC algorithms is presented, that relaxes the sufficient conditions for the most general proof of convergence for stochastic search algorithms in the literature (Anily …
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Learning two-dimensional spatial dynamics from experimental data
… bromide crystals, using a learning algorithm to search through a space of possible models in order to find an optimal description of the data. The space of possible models is a class of probabilistic cellular automaton rules, a rule which is inherently local. The traditional definition of a …
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Learning two-dimensional spatial dynamics from experimental data
… bromide crystals, using a learning algorithm to search through a space of possible models in order to find an optimal description of the data. The space of possible models is a class of probabilistic cellular automaton rules, a rule which is inherently local. The traditional definition of a …
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On the nature and origin of intuitive theories : learning, physics and psychology
… arguing that an algorithmic approach based on stochastic search can address several puzzles of learning, including the 'chicken and egg' problem of concept learning. Finally, I argue the need for a joint theory-space for reasoning about intuitive physics and intuitive psychology, and provide …
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Natively probabilistic computation
… of Boolean circuits, backtracking search and pure Lisp. I show how these tools let one compactly specify probabilistic generative models, generalize and parallelize widely used sampling algorithms like rejection sampling and Markov chain Monte Carlo, and solve difficult Bayesian …
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Feature reinforcement learning agents
… Learning (RL) is currently an active research area of Artificial Intelligence (AI) in which an agent interacts with an unknown environment in order to collect as much reward as possible. One of the most challenging problems in AI is the General Reinforcement Learning (GRL) problem where …
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Feature reinforcement learning agents
… Learning (RL) is currently an active research area of Artificial Intelligence (AI) in which an agent interacts with an unknown environment in order to collect as much reward as possible. One of the most challenging problems in AI is the General Reinforcement Learning (GRL) problem where …
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A Methodology For Minimizing The Oscillations In Supply Chains Using System Dynamics And Genetic Algorithms
… complex, non-linear dynamic behavior. GAs are stochastic search algorithms, based on the mechanics of natural selection and natural genetics, used to search complex and non-linear search spaces where traditional techniques may be unsuitable.
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Structured learning and inference with neural networks and generative models
… error-driven proposal mechanisms can speed up stochastic search for generative model inversion, first developing a symbolic model for inferring Boolean functions and Horn clause theories, and then a general-purpose neural network model for doing inference in continuous domains such as inverse …
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Biojutiklių atsako kreivių ir medžiagų koncentracijų regresinė analizė /
… optimal non-liner regression’ weights using stochastic search, and could determine the concentration of liquor according biosensors response curve While creating the model, we met model optimization problem. The optimal model means the optimal number of model’ weights and the values of …
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Statistical methods to study heterogeneity of treatment effects
… A key innovation of this test is to build stochastic search into the test statistic to detect signals that may not be linearly related to the multiple covariates. Simulations were performed to compare the proposed test with existing methods. Power calculation strategy was also developed for …
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Genetic programming applied to RFI mitigation in radio astronomy
… is a type of machine learning that employs a stochastic search of a solutions space, genetic operators, a fitness function, and multiple generations of evolved programs to resolve a user-defined task, such as the classification of data. At the time of this research, the application of machine …
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Continuous Low-Thrust Trajectory Optimization: Techniques and Applications
… for this first trajectory includes a global, stochastic search based on Adaptive Simulated Annealing; the fine tuning of optimization parameters — the local search — is accomplished by Quasi-Newton and Newton methods. Once an optimized trajectory has been obtained, we use system symmetry and …
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Multiobjective genetic algorithms with application to control engineering problems.
Genetic algorithms (GAs) are stochastic search techniques inspired by the principles of natural selection and natural genetics which have revealed a number of characteristics particularly useful for applications in optimization, engineering, and computer science, among other fields. In control …
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