178 research outputs found
Searching the Higgs with the Neurochip TOTEM
We show that neural network classifiers can be helpful in discriminating
Higgs production events from the huge background at LHC, assuming the case of a
mass value GeV. We use the high performance neurochip TOTEM,
trained by the Reactive Tabu Search algorithm (RTS), which could be used for
on-line purposes. Two different sets of input variables are compared.Comment: 4 pages,1 figure, requres espcrc2.sty and epsfig.sty. Work prsented
in The 5th Topical Seminar on ``The irresistible rise of the Standard
Model'', San Miniato, Tuscany, Italy, April 21-25 199
Resampling methods for parameter-free and robust feature selection with mutual information
Combining the mutual information criterion with a forward feature selection
strategy offers a good trade-off between optimality of the selected feature
subset and computation time. However, it requires to set the parameter(s) of
the mutual information estimator and to determine when to halt the forward
procedure. These two choices are difficult to make because, as the
dimensionality of the subset increases, the estimation of the mutual
information becomes less and less reliable. This paper proposes to use
resampling methods, a K-fold cross-validation and the permutation test, to
address both issues. The resampling methods bring information about the
variance of the estimator, information which can then be used to automatically
set the parameter and to calculate a threshold to stop the forward procedure.
The procedure is illustrated on a synthetic dataset as well as on real-world
examples
A cost-effective algorithm for the solution of engineering problems with particle swarm optimization
Ownership and control in a competitive industry
We study a differentiated product market in which an investor initially owns a controlling stake in one of two competing firms and may acquire a non-controlling or a controlling stake in a competitor, either directly using her own assets, or indirectly via the controlled firm. While industry profits are maximized within a symmetric two product monopoly, the investor attains this only in exceptional cases. Instead, she sometimes acquires a noncontrolling stake. Or she invests asymmetrically rather than pursuing a full takeover if she acquires a controlling one. Generally, she invests indirectly if she only wants to affect the product market outcome, and directly if acquiring shares is profitable per se. --differentiated products,separation of ownership and control,private benefits of control
Mutual information for the selection of relevant variables in spectrometric nonlinear modelling
Data from spectrophotometers form vectors of a large number of exploitable
variables. Building quantitative models using these variables most often
requires using a smaller set of variables than the initial one. Indeed, a too
large number of input variables to a model results in a too large number of
parameters, leading to overfitting and poor generalization abilities. In this
paper, we suggest the use of the mutual information measure to select variables
from the initial set. The mutual information measures the information content
in input variables with respect to the model output, without making any
assumption on the model that will be used; it is thus suitable for nonlinear
modelling. In addition, it leads to the selection of variables among the
initial set, and not to linear or nonlinear combinations of them. Without
decreasing the model performances compared to other variable projection
methods, it allows therefore a greater interpretability of the results
An Empirical Study of Off-line Configuration and On-line Adaptation in Operator Selection
Automating the process of finding good parameter settings is important in the design of high-performing algorithms. These automatic processes can generally be categorized into off-line and on-line methods. Off-line configuration consists in learning and selecting the best setting in a training phase, and usually fixes it while solving an instance. On-line adaptation methods on the contrary vary the parameter setting adaptively during each algorithm run. In this work, we provide an empirical study of both approaches on the operator selection problem, explore the possibility of varying parameter value by a non-adaptive distribution tuned off-line, and incorporate the off-line with on-line approaches. In particular, using an off-line tuned distribution to vary parameter values at runtime appears to be a promising idea for automatic configuration. © 2014 Springer International Publishing.SCOPUS: cp.kinfo:eu-repo/semantics/publishe
Self-organizing neural network for adaptive operator selection in evolutionary search
National Research Foundation (NRF) Singapore under its Corp Lab @ University schem
A case study of controlling crossover in a selection hyper-heuristic framework using the multidimensional knapsack problem
Hyper-heuristics are high-level methodologies for solving complex problems that operate on a search space of heuristics. In a selection hyper-heuristic framework, a heuristic is chosen from an existing set of low-level heuristics and applied to the current solution to produce a new solution at each point in the search. The use of crossover low-level heuristics is possible in an increasing number of general-purpose hyper-heuristic tools such as HyFlex and Hyperion. However, little work has been undertaken to assess how best to utilise it. Since a single-point search hyper-heuristic operates on a single candidate solution, and two candidate solutions are required for crossover, a mechanism is required to control the choice of the other solution. The frameworks we propose maintain a list of potential solutions for use in crossover. We investigate the use of such lists at two conceptual levels. First, crossover is controlled at the hyper-heuristic level where no problem-specific information is required. Second, it is controlled at the problem domain level where problem-specific information is used to produce good-quality solutions to use in crossover. A number of selection hyper-heuristics are compared using these frameworks over three benchmark libraries with varying properties for an NP-hard optimisation problem: the multidimensional 0-1 knapsack problem. It is shown that allowing crossover to be managed at the domain level outperforms managing crossover at the hyper-heuristic level in this problem domain. © 2016 Massachusetts Institute of Technolog
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