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TEAM Benchmark Problem 22
"... The TEAM benchmark problem 22 is an important optimization problem in electromagnetic design, which can be formulated as a constrained monoobjective problem or a multiobjective one with two objectives. In this paper, we propose a multiobjective version with three objectives, whose third objective i ..."
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The TEAM benchmark problem 22 is an important optimization problem in electromagnetic design, which can be formulated as a constrained monoobjective problem or a multiobjective one with two objectives. In this paper, we propose a multiobjective version with three objectives, whose third objective
TEAM Benchmark Problem 22
"... The TEAM benchmark problem 22 is an important optimization problem in electromagnetic design, which can be formulated as a constrained monoobjective problem or a multiobjective one with two objectives. In this paper, we propose a multiobjective version with three objectives, whose third objective i ..."
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The TEAM benchmark problem 22 is an important optimization problem in electromagnetic design, which can be formulated as a constrained monoobjective problem or a multiobjective one with two objectives. In this paper, we propose a multiobjective version with three objectives, whose third objective
PROBEN1  a set of neural network benchmark problems and benchmarking rules
, 1994
"... Proben1 is a collection of problems for neural network learning in the realm of pattern classification and function approximation plus a set of rules and conventions for carrying out benchmark tests with these or similar problems. Proben1 contains 15 data sets from 12 different domains. All datasets ..."
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Cited by 234 (0 self)
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Proben1 is a collection of problems for neural network learning in the realm of pattern classification and function approximation plus a set of rules and conventions for carrying out benchmark tests with these or similar problems. Proben1 contains 15 data sets from 12 different domains. All
Nonsymmetric benchmark problem
"... This problem concerns a pressure calculation for the incompressible NavierStokes equations. For the discretization a nite volume technique is used combined with boundary tted coordinates. This results in a structured matrix with at most 9 nonzero elements per row. The matrix looks like a discreti ..."
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This problem concerns a pressure calculation for the incompressible NavierStokes equations. For the discretization a nite volume technique is used combined with boundary tted coordinates. This results in a structured matrix with at most 9 nonzero elements per row. The matrix looks like a
Benchmark Problem Description
, 2004
"... The algorithm is described in a separate document entitled "Incompressible NavierStokes with Particles Algorithm Design Document " [1]. To evaluate the performance of the incompressible NavierStokes with particles code, we use a background °ow of a single vortex ring in three dimensions ..."
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in a 1m3 box. For this problem, the vorticity distribution is speci¯ed, and the initial velocity is then computed based on the initial vorticity ¯eld. The vortex ring is speci¯ed by a location of the center of the vortex ring (x0; y0; z0), the radius of the center of the local crosssection of the ring
ANALYSING SINGULARITIES OF A BENCHMARK PROBLEM
"... Abstract: The purpose of this paper is to analyze the singularities of a well known benchmark problem \Andrews ' squeezing mechanism". We show that for physically relevant parameter values this system admits singularities. The method is based on GrÄobner bases computations and ideal decomp ..."
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Abstract: The purpose of this paper is to analyze the singularities of a well known benchmark problem \Andrews ' squeezing mechanism". We show that for physically relevant parameter values this system admits singularities. The method is based on GrÄobner bases computations and ideal
Analying the singularities of a benchmark problem
, 2006
"... The purpose of this paper is to analyze the singularities of a well known benchmark problem "Andrews' squeezing mechanism". We show that for physically relevant parameter values this system admits singularities. The method is based on Gröbner bases computations and ideal decompositi ..."
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The purpose of this paper is to analyze the singularities of a well known benchmark problem "Andrews' squeezing mechanism". We show that for physically relevant parameter values this system admits singularities. The method is based on Gröbner bases computations and ideal
Human benchmarks on ai's benchmark problems
 In Proceedings of the Fifteenth Annual Conference of the Cognitive Science Society. (pp
, 1993
"... Default reasoning occurs when the available information does not deductively guarantee the truth of the conclusion; and the conclusion is nonetheless correctly arrived at. The formalisms that have been developed in Artificial Intelligence to capture this mode of reasoning have suffered from a lack o ..."
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Cited by 21 (2 self)
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of agreement as to which nonmonotonic inferences should be considered correct; and so Lifschitz 1989 produced a set of “Nonmonotonic Benchmark Problems ” which all future formalisms are supposed to honor. The present work investigates the extent to which humans follow the prescriptions set out
Benchmarking Least Squares Support Vector Machine Classifiers
 NEURAL PROCESSING LETTERS
, 2001
"... In Support Vector Machines (SVMs), the solution of the classification problem is characterized by a (convex) quadratic programming (QP) problem. In a modified version of SVMs, called Least Squares SVM classifiers (LSSVMs), a least squares cost function is proposed so as to obtain a linear set of eq ..."
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Cited by 479 (46 self)
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In Support Vector Machines (SVMs), the solution of the classification problem is characterized by a (convex) quadratic programming (QP) problem. In a modified version of SVMs, called Least Squares SVM classifiers (LSSVMs), a least squares cost function is proposed so as to obtain a linear set
The multiobjective genetic . . . benchmark problems  an analysis
, 2001
"... The multiobjective genetic algorithm (MOGA) has been applied to various realworld problems in a variety of fields, most prominently in control systems engineering, with considerable success. However, a recent empirical analysis of multiobjective evolutionary algorithms (MOEAs) has suggested that a ..."
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across the benchmark problems. This does not suggest that MOGA is the ‘best ’ MOEA, rather that a considered implementation of the methodology is required in order to reap full rewards. This study also questions the effectiveness of the traditional fitness sharing method of niching, with respect
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