The proceedings contain 34 papers. The topics discussed include: an adaptive trust-region approach for nonlinear stochastic optimization with an application in discrete choice theory;approximation algorithms for 2-sta...
The proceedings contain 34 papers. The topics discussed include: an adaptive trust-region approach for nonlinear stochastic optimization with an application in discrete choice theory;approximation algorithms for 2-stage and multi-stage stochastic optimization;assessing solution quality in stochastic programs;average case and smoothed competitive analysis of the multi-level feedback algorithm;average-case competitive analyses for ski-rental problems;deferment control for reoptimization - how to find fair reoptimized dispatches;disruption management and planning with uncertainties in aircraft planning;facility location with uncertain demand and economies of scale;getting rid of stochasticity: applicable sometimes;marginal productivity index policies for scheduling restless bandits with switching penalties;and models and algorithms for stochastic online scheduling.
algorithms for preprocessing databases with incomplete and imprecise data are seldom studied. For the most part, we lack numerical tools to quantify the mutual information between fuzzy random variables. Therefore, th...
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algorithms for preprocessing databases with incomplete and imprecise data are seldom studied. For the most part, we lack numerical tools to quantify the mutual information between fuzzy random variables. Therefore, these algorithms (discretization, instance selection, feature selection, etc.) have to use crisp estimations of the interdependency between continuous variables, whose application to vague datasets is arguable. In particular, when we select features for being used in fuzzy rule-based classifiers, we often use a mutual information-based ranking of the relevance of inputs. But, either with crisp or fuzzy data, fuzzy rule-based systems route the input through a fuzzification interface. The fuzzification process may alter this ranking, as the partition of the input data does not need to be optimal. In our opinion, to discover the most important variables for a fuzzy rule-based system, we want to compute the mutual information between the fuzzified variables, and we should not assume that the ranking between the crisp variables is the best one. In this paper we address these problems, and propose an extended definition of the mutual information between two fuzzified continuous variables. We also introduce a numerical algorithm for estimating the mutual information from a sample of vague data. We will show that this estimation can be included in a feature selection algorithm, and also that, in combination with a genetic optimization, the same definition can be used to obtain the most informative fuzzy partition for the data. Both applications will be exemplified with the help of some benchmark problems. (C) 2008 Elsevier Inc. All rights reserved.
Problems of incompleteinformation include a component of unknown information. We investigate such problems through a study of the bidding phase in the game of Bridge. In particular, we would like to apply genetic alg...
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ISBN:
(纸本)9781932415667
Problems of incompleteinformation include a component of unknown information. We investigate such problems through a study of the bidding phase in the game of Bridge. In particular, we would like to apply genetic algorithms to the Bridge bidding problem. Genetic algorithms, however, require a fitness function appropriate for the problem. Therefore, in this paper, we first attack the optimization problem of finding the maximum number of tricks that can be taken in a bridge hand with optimal play and with complete information. Solutions from this optimization problem will subsequently be used as fitness functions in applying genetic algorithms to the bidding problem.
In October 2003, sixteen boats set off from La Gomera in the Canary Islands headed for Barbados 4800 km distant. Each boat was manned by two oarsmen who were competing in the Transatlantic Challenge, an ocean rowing e...
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Stochastic optimization problems provide a means to model uncertainty in the input data where the uncertainty is modeled by a probability distribution over the possible realizations of the data. We consider the well-s...
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