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 326 for “"search space"”.
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Exploring the job-shop search space with genetic algorithms
Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1997.
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ATPG based Preimage Computation: Efficient Search Space Pruning using ZBDD
… methods, since they must explore an exponential search space. In order to reduce this temporal explosion of SAT/ATPG based methods, efficient learning techniques are needed. Conventional ATPG aims at computing a single solution for its objective. In preimage computation, we must enumerate all …
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Meta-level learning for the effective reduction of model search space.
… hyper-parameter optimization. The goal of this research is to investigate model selection and hyper-parameter optimization approaches of automatic machine learning in general and the challenges associated with them. In machine learning pipeline there are several phases where Meta-learning can be …
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Search-space Aware Learning Techniques for Unbounded Model Checking and Path Delay Testing
… structure and take advantage of the repeated search space, thereby alleviating the memory required and time taken to solve these problems. In this dissertation, we exploit Automatic Test Pattern Generation (ATPG) for Unbounded Model Checking (UMC). In order to perform unbounded model checking, …
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Issues of Real Time Information Retrieval in Large, Dynamic and Heterogeneous Search Spaces
… information, the relevancy ranking of the search results also changes. Current methods in information retrieval, which are based on offline indexing, are not efficient in such dynamic search spaces and cannot quickly provide the most current results. Due to the explosive growth of the …
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An analysis of combinatorial search spaces for a class of NP-hard problems
… maximize (or minimize) ƒ. Many combinatorial search algorithms employ some perturbation operator to hill-climb in the search space. Such perturbative local search algorithms are state of the art for many classes of NP-hard combinatorial optimization problems such as maximum k-satisfiability, …
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On Enhancing Deterministic Sequential ATPG
… "don't care" value. An approach for reducing the search space of the ATPG was introduced. The technique can significantly reduce the size of the search space but cannot ensure the completeness of the search. Results on ISCAS–85 benchmark circuits show that all of the proposed techniques allow for …
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INFORMED EXPLORATION ALGORITHMS FOR ROBOT MOTION PLANNING AND LEARNING
… algorithms avoid a priori discretization of the search-space by generating random samples and building a graph online. While the recent advances in this area endow these randomized planners with asymptotic optimality, their slow convergence rate still remains a challenge. One of the reasons for …
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Automatic physical database design : recommending materialized views
… are used to support the construction of a search space for view selection problems. We proposed an approach for constructing a search space based on identifying maximum commonalities among queries and on rewriting queries using views. These commonalities are used to define candidate views …
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Using Conflict and Support Counts for Variable and Value Ordering in CSPs
… as scheduling and timetabling in operations research, map-coloring problem and Boolean satisfiability are some of the examples that can be represented and solved with a CSP framework. Solving a CSP is about searching for a solution in a huge search space. Very often, much search efforts are …
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Evolutionary Design of Artificial Neural Networks Using a Descriptive Encoding Language
… (neuroevolution) has generated much recent research both because successful approaches will facilitate wide-spread use of intelligent systems based on neural networks, and because it will shed light on our understanding of how "real" neural networks may have evolved. The main challenge in …
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Experimental analysis on the operation of Particle Swarm Optimization
… the swarms particles are in good regions of the search space with the potential to make more progress, the introduction of perturbations to the pbest positions can lead to significant improvements in the performance of standard Particle Swarm Optimization. The pbest perturbation has been …
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Exploring the impact of novelty and objective-directed evolution in company with MAP-Elites and HyperNEAT
… with HyperNEAT while varying the evolutionary search directive between an objective and non-objective search, and a hybrid approach. Objective search refers to evolutionary algorithms that explicitly optimize a predetermined performance metric, whereas nonobjective search refers to evolutionary …
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Efficient Algorithms and Systems for Tiny Deep Learning
… TinyNAS adopts a two-stage neural architecture search approach that first optimizes the search space to fit the resource constraints, then specializes the network architecture in the optimized search space. TinyNAS can automatically handle diverse constraints (i.e. device, latency, energy, …
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Characterization of Search Spaces and Effects on Machine Learning
… the effectiveness of results. Focusing on the search space of problems, a rigorous study was conducted to generate an in-depth understanding of the impact of search space characteristics to the performance of a ML algorithm, specifically a Genetic Algorithm (GA). The effects of specific problem …
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Searching for patterns in Conway's Game of Life
… patterns in Life is difficult due to the large search space. Current search algorithms use an explorative approach based on the rules of the game, but this can only sample a small fraction of the search space. More recently, people have used Sat solvers to search for patterns. These solvers are …
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A language and a system for program optimization
… The Locus language allows the definition of a search space combined with the programming of optimization sequences separated from the application’s reference code. After all, we present an approach for performance portability. Our thesis is that we can ameliorate the difficulty of optimizing …
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Analyses of algorithm performance for an oversubscribed scheduling problem
… in the set are designed to traverse the same search space: solutions are represented as permutations of tasks; a greedy schedule builder converts the permutation into a schedule by assigning a start time and resources to the requests in the order in which they appear in the permutation. …
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Discovering Group Differences from Qualitative and Quantitative Attributes Using Contrast Set Mining with Discretization and Measures of Interestingness
… in the dataset. We demonstrate how we build our search space of all possible contrast sets from the attributes, and attribute-values in the dataset. We then present the COSINE algorithm, for traversing the search space of possible contrast sets. We show how the COSINE algorithm generates all …
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Improved cuckoo search based neural network learning algorithms for data classification
… Among the conventional methods, some researchers prefer Levenberg-Marquardt (LM) because of its convergence speed and performance. On the other hand, LM algorithms which are derivative based algorithms still face a risk of getting stuck in local minima. Recently, a novel meta-heuristic …
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