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Showing 1 to 13 of 13 for “"ranking and selection"”.
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Methods of Nonparametric Multivariate Ranking and Selection
<p>In a Ranking and Selection problem, a collection of k populations is given which follow some (partially) unknown probability distributions. The problem is to select the "best" of the k populations where "best" is well defined in terms of some unknown population parameter. In many univariate …
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Using metaheuristics with the ranking and selection indifference-zone procedure MMY in problems with many objectives
… examines the integration of multi-objective ranking and selection (MORS) techniques with metaheuristic algorithms for solving large-scale, stochastic, multi-objective optimisation (MOO) problems. Many real-world decision problems involve multiple competing objectives, stochastic simulation …
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ON SOLVING MULTI-OBJECTIVE SIMULATION OPTIMIZATION BY OPTIMAL COMPUTING BUDGET ALLOCATION AND RANDOM SEARCH
… conflicting objectives, analytical models and closed-form optimal solutions are usually hard to formulate and derive. These issues can be addressed by multi-objective simulation optimization, which employs efficient simulation to evaluate solutions' performance and such information is …
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FORMULATION OF DETECTION STRATEGIES IN IMAGES
… runways by fusing Synthetic Vision System (SVS) and Enhanced Vision System (EVS) images. A novel procedure is developed to accurately detect runways and horizons and also enhance runway surrounding areas by fusing enhanced vision system (EVS) and synthetic vision system (SVS) images of the runway …
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OPTIMAL COMPUTING BUDGET ALLOCATION FOR SIMULATION BASED OPTIMIZATION AND COMPLEX DECISION MAKING
… budget constraint. It is not only an efficient ranking and selection procedure for simulation problems with finite candidate solutions but also an attractive concept of resource allocation under stochastic environment. In this thesis, the framework of optimal computing budget allocation is …
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Simulation-Based Decision Making with Streaming Data
… performance cannot be evaluated analytically and must instead be estimated through simulation. At the same time, the stochastic inputs driving these systems are often unknown and must be inferred from data, which may be collected gradually over time. These challenges motivate the development …
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OPTIMIZATION UNDER STOCHASTIC ENVIRONMENT
… as control engineering, operations research, and computer science. It has found wide applications ranging from path planning (civil engineering) and tool-life testing (industrial engineering) to Go-playing artificial intelligence (computer science). However, SO is usually a hard problem …
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Sampling Laws for Stochastically Constrained Simulation Optimization on Finite Sets
… based on a "stochastic" objective function and subject to multiple "stochastic" constraints. In this context, we characterize the asymptotically optimal sample allocation that maximizes the rate at which the probability of false selection tends to zero in two scenarios: first in the context …
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Search Techniques for Evolutionary Constrained Optimization
… topic in the optimization, operation research, and computer science domains. Over the last few decades, evolutionary algorithms (EAs) have been widely adopted to solve such problems. However the main search operators of EAs, such as crossover and mutation, are usually the same for both …
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Essays on Model Selection Uncertainty and Model Averaging: Computational and Empirical Work with Beta Regression, Multiple Linear Regression with ARMA Innovations, and the Minimum Description Length Principle
Uncertainty in model selection is under-explored and frequently resolved non-rigorously through beliefs about generalizability, practical usefulness, and computational ease. This is problematic as model selection routinely admits multiple models which imposes extra uncertainty on all post-selection …
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Evaluation of the Design of a Family Practice Healthcare Clinic Using Discrete-Event Simulation
With increased pressures from governmental and insurance agencies, today's physician devotes less time to patient care and more time to administration. To alleviate this problem, Biological & Popular Culture, Inc. (Biopop) proposed the building of partnerships with healthcare professionals to …
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Dimensionality Reduction and Fusion Strategies for the Design of Parametric Signal Classifiers
… to overcome the curse of dimensionality and information fusion to improve classification by exploiting complementary information from multiple sensors or multiple classifiers. Dimensionality reduction is achieved by introducing a strategy to rank and select a subset of principal component …