Results 1 
9 of
9
Efficient topk query evaluation on probabilistic data
 in ICDE
, 2007
"... Modern enterprise applications are forced to deal with unreliable, inconsistent and imprecise information. Probabilistic databases can model such data naturally, but SQL query evaluation on probabilistic databases is difficult: previous approaches have either restricted the SQL queries, or computed ..."
Abstract

Cited by 137 (26 self)
 Add to MetaCart
Modern enterprise applications are forced to deal with unreliable, inconsistent and imprecise information. Probabilistic databases can model such data naturally, but SQL query evaluation on probabilistic databases is difficult: previous approaches have either restricted the SQL queries, or computed approximate probabilities, or did not scale, and it was shown recently that precise query evaluation is theoretically hard. In this paper we describe a novel approach, which computes and ranks efficiently the topk answers to a SQL query on a probabilistic database. The restriction to topk answers is natural, since imprecisions in the data often lead to a large number of answers of low quality, and users are interested only in the answers with the highest probabilities. The idea in our algorithm is to run in parallel several MonteCarlo simulations, one for each candidate answer, and approximate each probability only to the extent needed to compute correctly the topk answers. The algorithms is in a certain sense provably optimal and scales to large databases: we have measured running times of 5 to 50 seconds for complex SQL queries over a large database (10M tuples of which 6M probabilistic). Additional contributions of the paper include several optimization techniques, and a simple data model for probabilistic data that achieves completeness by using SQL views. 1
A knowledgegradient policy for sequential information collection
 SIAM J. on Control and Optimization
"... Abstract. In a sequential Bayesian ranking and selection problem with independent normal populations and common known variance, we study a previously introduced measurement policy which we refer to as the knowledgegradient policy. This policy myopically maximizes the expected increment in the value ..."
Abstract

Cited by 18 (15 self)
 Add to MetaCart
Abstract. In a sequential Bayesian ranking and selection problem with independent normal populations and common known variance, we study a previously introduced measurement policy which we refer to as the knowledgegradient policy. This policy myopically maximizes the expected increment in the value of information in each time period, where the value is measured according to the terminal utility function. We show that the knowledgegradient policy is optimal both when the horizon is a single time period and in the limit as the horizon extends to infinity. We show furthermore that, in some special cases, the knowledgegradient policy is optimal regardless of the length of any given fixed total sampling horizon. We bound the knowledgegradient policy’s suboptimality in the remaining cases, and show through simulations that it performs competitively with or significantly better than other policies.
Parameter Tuning for Configuring and Analyzing Evolutionary Algorithms
 Swarm and Evolutionary Computation
, 2011
"... In this paper we present a conceptual framework for parameter tuning, provide a survey of tuning methods, and discuss related methodological issues. The framework is based on a threetier hierarchy of a problem, an evolutionary algorithm (EA), and a tuner. Furthermore, we distinguish problem instanc ..."
Abstract

Cited by 8 (0 self)
 Add to MetaCart
In this paper we present a conceptual framework for parameter tuning, provide a survey of tuning methods, and discuss related methodological issues. The framework is based on a threetier hierarchy of a problem, an evolutionary algorithm (EA), and a tuner. Furthermore, we distinguish problem instances, parameters, and EA performance measures as major factors, and discuss how tuning can be directed to algorithm performance and/or robustness. For the survey part we establish different taxonomies to categorize tuning methods and review existing work. Finally, we elaborate on how tuning can improve methodology by facilitating wellfunded experimental comparisons and algorithm analysis.
Using Entropy for Parameter Analysis of Evolutionary Algorithms
"... Abstract — Evolutionary Algorithms (EA) form a rich class of stochastic search methods that share the basic principles of incrementally improving the quality of a set of candidate solutions by means of variation and selection [10], [8]. Such variation and selection operators often require parameters ..."
Abstract

Cited by 3 (2 self)
 Add to MetaCart
Abstract — Evolutionary Algorithms (EA) form a rich class of stochastic search methods that share the basic principles of incrementally improving the quality of a set of candidate solutions by means of variation and selection [10], [8]. Such variation and selection operators often require parameters to be specified. Finding a good set of parameter values is a nontrivial problem in itself, furthermore some EA parameters are more relevant than others in the sense that choosing different values for them affects EA performance more than for the other parameters. In this chapter we explain the notion of entropy and discuss how entropy can disclose important information on EA parameters, in particular, about their relevance. We describe an algorithm that is able to estimate the entropy of EA parameters and we present a showcase, based on extensive experimentation, to demonstrate the usefulness of this approach
Knowledgegradient methods for statistical learning
, 2009
"... We consider the class of fully sequential Bayesian information collection problems, a class that includes ranking and selection problems, multiarmed bandit problems, and many others. Although optimal policies for such problems are generally known to exist and to satisfy Bellman’s recursion, the cur ..."
Abstract

Cited by 3 (2 self)
 Add to MetaCart
We consider the class of fully sequential Bayesian information collection problems, a class that includes ranking and selection problems, multiarmed bandit problems, and many others. Although optimal policies for such problems are generally known to exist and to satisfy Bellman’s recursion, the curses of dimensionality prevent us from actually computing them except in a few very special cases. Motivated by this difficulty, we develop a general class of practical and theoretically wellfounded information collection policies known as knowledgegradient (KG) policies. KG policies have several attractive qualities: they are myopically optimal in general; they are asymptotically optimal in a broad class of problems; they are flexible and may be computed easily in a broad class of problems; and they perform well numerically in several wellstudied ranking and selection problems compared with other stateoftheart policies designed specifically for these problems. iii Acknowledgements I am grateful to many people for their help in completing my PhD. First, I would like to thank my advisor, Professor Warren Powell, for his ability to choose problems, his untiring availability for questions, his high expectations, and the wonderful
Particle swarm optimization and sequential sampling in noisy environments
 Proceedings 6th Metaheuristics International Conference (MIC2005
, 2005
"... For many practical optimization problems, the evaluation of a solution is subject to noise, and optimization heuristics capable of handling such noise are needed. In this paper, we examine the influence of noise on particle swarm optimization and demonstrate that the resulting stagnation can not be ..."
Abstract

Cited by 2 (2 self)
 Add to MetaCart
For many practical optimization problems, the evaluation of a solution is subject to noise, and optimization heuristics capable of handling such noise are needed. In this paper, we examine the influence of noise on particle swarm optimization and demonstrate that the resulting stagnation can not be removed by parameter optimization alone, but requires a reduction of noise through averaging over multiple samples. In order to reduce the number of required samples, we propose a combination of particle swarm optimization and a statistical sequential selection procedure, called optimal computing budget allocation, which attempts to distribute a given number of samples in the most effective way. Experimental results show that this new algorithm indeed outperforms the other alternatives.
Optimization for Simulation: LAD Accelerator
, 2007
"... The goal of this paper is to address the problem of evaluating the performance of a system running under unknown values for its stochastic parameters. A new approach called LAD for Simulation, based on simulation and classification software, is presented. It uses a number of simulations with very fe ..."
Abstract

Cited by 2 (0 self)
 Add to MetaCart
The goal of this paper is to address the problem of evaluating the performance of a system running under unknown values for its stochastic parameters. A new approach called LAD for Simulation, based on simulation and classification software, is presented. It uses a number of simulations with very few replications and record the mean value of directly measurable quantities (called observables). These observables are used as input to a classification model that produces a prediction for the performance of the system. Application to an assembletoorder system from the literature is described and detailed results illustrate the strength of the method.
Proceedings of the 2007 INFORMS Simulation Society Research Workshop EFFICIENT SAMPLING IN INTERACTIVE MULTICRITERIA SELECTION
"... Selection procedures are used in a variety of applications to select the best of a finite set of alternatives. Thereby ‘best ’ is defined with respect to the largest mean, but the mean is inferred with statistical sampling, as in simulation optimization. However, many practical selection problems in ..."
Abstract
 Add to MetaCart
Selection procedures are used in a variety of applications to select the best of a finite set of alternatives. Thereby ‘best ’ is defined with respect to the largest mean, but the mean is inferred with statistical sampling, as in simulation optimization. However, many practical selection problems involve multiple conflicting criteria, so that the definition of “best ” depends on the user’s preferences. Unfortunately, the user can usually not fully specify his or her preferences before having seen the alternatives. Thus, we propose here an interactive procedure, gathering some information on the alternatives, asking the user to specify preferences more precisely, and then gathering more information until the user has converged on a single solution with sufficient confidence. The method uses an extension of Optimal Computing Budget Allocation to allocate samples efficiently. 1
THE KNOWLEDGEGRADIENT STOPPING RULE FOR RANKING AND SELECTION
"... We consider the ranking and selection of normal means in a fully sequential Bayesian context. By considering the sampling and stopping problems jointly rather than separately, we derive a new composite stopping/sampling rule. The sampling component of the derived composite rule is the same as the pr ..."
Abstract
 Add to MetaCart
We consider the ranking and selection of normal means in a fully sequential Bayesian context. By considering the sampling and stopping problems jointly rather than separately, we derive a new composite stopping/sampling rule. The sampling component of the derived composite rule is the same as the previously introduced LL1 sampling rule, but the stopping rule is new. This new stopping rule significantly improves the performance of LL1 as compared to its performance under the best other generally known adaptive stopping rule, EOC Bonf, outperforming it in every case tested. 1