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You searched for subject:(NK landscapes). Showing records 1 – 2 of 2 total matches.

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University of Vermont

1. Manukyan, Narine. Analysis and Modeling of Quality Improvement on Clinical Fitness Landscapes.

Degree: PhD, Computer Science, 2014, University of Vermont

Widespread unexplained variations in clinical practices and patient outcomes, together with rapidly growing availability of data, suggest major opportunities for improving the quality of medical care. One way that healthcare practitioners try to do that is by participating in organized healthcare quality improvement collaboratives (QICs). In QICs, teams of practitioners from different hospitals exchange information on clinical practices, with the aim of improving health outcomes at their own institutions. However, what works in one hospital may not work in others with different local contexts, due to non-linear interactions among various demographics, treatments, and practices. I.e., the clinical landscape is a complex socio-technical system that is difficult to search. In this dissertation we develop methods for analysis and modeling of complex systems, and apply them to the problem of healthcare improvement. Searching clinical landscapes is a multi-objective dynamic problem, as hospitals simultaneously optimize for multiple patient outcomes. We first discuss a general method we developed for finding which changes in features may be associated with various changes in outcomes at different points in time with different delays in affect. This method correctly inferred interactions on synthetic data, however the complexity and incompleteness of the real hospital dataset available to us limited the usefulness of this approach. We then discuss an agent-based model (ABM) of QICs to show that teams comprising individuals from similar institutions outperform those from more diverse institutions, under nearly all conditions, and that this advantage increases with the complexity of the landscape and the level of noise in assessing performance. We present data from a network of real hospitals that provides encouraging evidence of a high degree of similarity in clinical practices among hospitals working together in QIC teams. Based on model outcomes, we propose a secure virtual collaboration system that would allow hospitals to efficiently identify potentially better practices in use at other institutions similar to theirs, without any institutions having to sacrifice the privacy of their own data. To model the search for quality improvement in clinical fitness landscapes, we need benchmark landscapes with tunable feature interactions. NK landscapes have been the classic benchmarks for modeling landscapes with epistatic interactions, but the ruggedness is only tunable in discrete jumps. Walsh polynomials are more finely tunable than NK landscapes, but are only defined on binary alphabets and, in general, have unknown global maximum and minimum. We define a different subset of interaction models that we dub as NM landscapes. NM landscapes are shown to have smoothly tunable ruggedness and difficulty and known location and value of global maxima. With additional constraints, we can also determine the location and value of the global minima. The proposed NM… Advisors/Committee Members: Margaret J. Eppstein.

Subjects/Keywords: agent based modeling; epistasis; NK landscapes; quality improvement; rugged fitness landscapes; team learning; Computer Sciences

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APA (6th Edition):

Manukyan, N. (2014). Analysis and Modeling of Quality Improvement on Clinical Fitness Landscapes. (Doctoral Dissertation). University of Vermont. Retrieved from https://scholarworks.uvm.edu/graddis/253

Chicago Manual of Style (16th Edition):

Manukyan, Narine. “Analysis and Modeling of Quality Improvement on Clinical Fitness Landscapes.” 2014. Doctoral Dissertation, University of Vermont. Accessed March 03, 2021. https://scholarworks.uvm.edu/graddis/253.

MLA Handbook (7th Edition):

Manukyan, Narine. “Analysis and Modeling of Quality Improvement on Clinical Fitness Landscapes.” 2014. Web. 03 Mar 2021.

Vancouver:

Manukyan N. Analysis and Modeling of Quality Improvement on Clinical Fitness Landscapes. [Internet] [Doctoral dissertation]. University of Vermont; 2014. [cited 2021 Mar 03]. Available from: https://scholarworks.uvm.edu/graddis/253.

Council of Science Editors:

Manukyan N. Analysis and Modeling of Quality Improvement on Clinical Fitness Landscapes. [Doctoral Dissertation]. University of Vermont; 2014. Available from: https://scholarworks.uvm.edu/graddis/253


Colorado State University

2. Chen, Wenxiang. Step toward constant time local search for optimizing pseudo boolean functions, A.

Degree: MS(M.S.), Computer Science, 2013, Colorado State University

Pseudo Boolean Functions (PBFs) are the objective functions for a wide class of hard optimization problems, such as MAX-SAT and MAX-CUT. Since these problems are NP-Hard, researchers and practitioners rely on incomplete solvers, such as Stochastic Local Search (SLS), for large problems. Best-Improvement Local Search (BILS) is a common form of SLS, which always takes the move yielding the highest improvement in the objective function. Generally, the more runtime SLS is given, the better solution can be obtained. This thesis aims at algorithmically accelerating SLS for PBFs using Walsh Analysis. The contributions of this thesis are threefold. First, a general approach for executing an approximate best-improvement move in constant time on average using Walsh analysis, "Walsh-LS", is described. Conventional BILS typically requires examining all n neighbors to decide which move to take, given the number of variables is n. With Walsh analysis, however, we can determine which neighbors need to be checked. As long as the objective function is epistatically bounded by a constant k (k is the number of variables per subfunctions), the number of neighbors that need to be checked is constant regardless of problem size. An impressive speedup of runtime (up to 449 times) is observed in our empirical studies. Second, in the context of Walsh-LS, we empirically study two key components of SLS from the perspectives of both efficiency and effectiveness: 1) Local optimum escape method: hard random or soft restarts; 2) Local search strategy: first-improvement or best-improvement. Lastly, on average we can perform approximate BILS using the mean over a Hamming region of arbitrary radius as a surrogate objective function. Even though the number of points is exponential in the radius of the Hamming region, BILS using the mean value of points in the Hamming region as a surrogate objective function can still take each move in time independent of n on average. According to our empirical studies, using the average over a Hamming region as a surrogate objective function can yield superior performance results on neutral landscapes like NKq-landscapes. Advisors/Committee Members: Whitley, L. Darrell (advisor), Howe, Adele E. (advisor), Cheney, Margaret (committee member).

Subjects/Keywords: complexity per move; stochastic local search; pseudo Boolean optimization; NK-landscapes

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Chen, W. (2013). Step toward constant time local search for optimizing pseudo boolean functions, A. (Masters Thesis). Colorado State University. Retrieved from http://hdl.handle.net/10217/81007

Chicago Manual of Style (16th Edition):

Chen, Wenxiang. “Step toward constant time local search for optimizing pseudo boolean functions, A.” 2013. Masters Thesis, Colorado State University. Accessed March 03, 2021. http://hdl.handle.net/10217/81007.

MLA Handbook (7th Edition):

Chen, Wenxiang. “Step toward constant time local search for optimizing pseudo boolean functions, A.” 2013. Web. 03 Mar 2021.

Vancouver:

Chen W. Step toward constant time local search for optimizing pseudo boolean functions, A. [Internet] [Masters thesis]. Colorado State University; 2013. [cited 2021 Mar 03]. Available from: http://hdl.handle.net/10217/81007.

Council of Science Editors:

Chen W. Step toward constant time local search for optimizing pseudo boolean functions, A. [Masters Thesis]. Colorado State University; 2013. Available from: http://hdl.handle.net/10217/81007

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