Results 1  10
of
881
Evolutionary Algorithms for Constrained Parameter Optimization Problems
 Evolutionary Computation
, 1996
"... Evolutionary computation techniques have received a lot of attention regarding their potential as optimization techniques for complex numerical functions. However, they have not produced a significant breakthrough in the area of nonlinear programming due to the fact that they have not addressed the ..."
Abstract

Cited by 315 (18 self)
 Add to MetaCart
disappointing. In this paper we (1) discuss difficulties connected with solving the general nonlinear programming problem, (2) survey several approaches which have emerged in the evolutionary computation community, and (3) provide a set of eleven interesting test cases, which may serve as a handy reference
A general approximation technique for constrained forest problems
 SIAM J. COMPUT.
, 1995
"... We present a general approximation technique for a large class of graph problems. Our technique mostly applies to problems of covering, at minimum cost, the vertices of a graph with trees, cycles, or paths satisfying certain requirements. In particular, many basic combinatorial optimization proble ..."
Abstract

Cited by 414 (21 self)
 Add to MetaCart
We present a general approximation technique for a large class of graph problems. Our technique mostly applies to problems of covering, at minimum cost, the vertices of a graph with trees, cycles, or paths satisfying certain requirements. In particular, many basic combinatorial optimization
Multiple kernel learning, conic duality, and the SMO algorithm
 In Proceedings of the 21st International Conference on Machine Learning (ICML
, 2004
"... While classical kernelbased classifiers are based on a single kernel, in practice it is often desirable to base classifiers on combinations of multiple kernels. Lanckriet et al. (2004) considered conic combinations of kernel matrices for the support vector machine (SVM), and showed that the optimiz ..."
Abstract

Cited by 445 (31 self)
 Add to MetaCart
that the optimization of the coefficients of such a combination reduces to a convex optimization problem known as a quadraticallyconstrained quadratic program (QCQP). Unfortunately, current convex optimization toolboxes can solve this problem only for a small number of kernels and a small number of data points
Evolutionary Techniques for Constrained Optimization Problems
, 1999
"... : An Evolutionary Algorithm to solve general constrained optimization problems is proposed in this paper. Mathematical programming problems such as linear, nonlinear, integer, boolean and mixed programming problems can be solved by using this technique. Some important characteristics of the Evolutio ..."
Abstract

Cited by 24 (0 self)
 Add to MetaCart
: An Evolutionary Algorithm to solve general constrained optimization problems is proposed in this paper. Mathematical programming problems such as linear, nonlinear, integer, boolean and mixed programming problems can be solved by using this technique. Some important characteristics
On the Learnability and Design of Output Codes for Multiclass Problems
 In Proceedings of the Thirteenth Annual Conference on Computational Learning Theory
, 2000
"... . Output coding is a general framework for solving multiclass categorization problems. Previous research on output codes has focused on building multiclass machines given predefined output codes. In this paper we discuss for the first time the problem of designing output codes for multiclass problem ..."
Abstract

Cited by 228 (6 self)
 Add to MetaCart
problems. For the design problem of discrete codes, which have been used extensively in previous works, we present mostly negative results. We then introduce the notion of continuous codes and cast the design problem of continuous codes as a constrained optimization problem. We describe three optimization
Convex Approximations of Chance Constrained Programs
"... We consider a chance constrained problem, where one seeks to minimize a convex objective over solutions satisfying, with a given (close to one) probability, a system of randomly perturbed convex constraints. Our goal is to build a computationally tractable approximation of this (typically intractabl ..."
Abstract

Cited by 127 (6 self)
 Add to MetaCart
intractable) problem, i.e., an explicitly given convex optimization program with the feasible set contained in the one of the chance constrained problem. We construct a general class of such convex conservative approximations of the corresponding chance constrained problem. Moreover, under the assumptions
Multiple Lagrange Multiplier Method for Constrained Evolutionary Optimization
 Eds.): Simulated Evolution and Learning, 1998, Lecture Notes in Artificial Intelligence
, 1999
"... . One of the wellknown problems in evolutionary search for solving optimization problem is the premature convergence. The general constrained optimization techniques such as hybrid evolutionary programming, twophase evolutionary programming, and Evolian algorithms are not safe from the same probl ..."
Abstract

Cited by 2 (0 self)
 Add to MetaCart
. One of the wellknown problems in evolutionary search for solving optimization problem is the premature convergence. The general constrained optimization techniques such as hybrid evolutionary programming, twophase evolutionary programming, and Evolian algorithms are not safe from the same
LARGESCALE LINEARLY CONSTRAINED OPTIMIZATION
, 1978
"... An algorithm for solving largescale nonlinear ' programs with linear constraints is presented. The method combines efficient sparsematrix techniques as in the revised simplex method with stable quasiNewton methods for handling the nonlinearities. A generalpurpose production code (MINOS) is ..."
Abstract

Cited by 112 (21 self)
 Add to MetaCart
An algorithm for solving largescale nonlinear ' programs with linear constraints is presented. The method combines efficient sparsematrix techniques as in the revised simplex method with stable quasiNewton methods for handling the nonlinearities. A generalpurpose production code (MINOS
Autocalibration and the absolute quadric
 in Proc. IEEE Conf. Computer Vision, Pattern Recognition
, 1997
"... We describe a new method for camera autocalibration and scaled Euclidean structure and motion, from three or more views taken by a moving camera with fixed but unknown intrinsic parameters. The motion constancy of these is used to rectify an initial projective reconstruction. Euclidean scene structu ..."
Abstract

Cited by 248 (7 self)
 Add to MetaCart
easily. The nonlinear method is stabler, faster, more accurate and more general than the quasilinear one. It is based on a general constrained optimization technique — sequential quadratic programming — that may well be useful in other vision problems.
On the Use of NonStationary Penalty Functions to Solve Nonlinear Constrained Optimization Problems with GA's
 In
, 1994
"... In this paper we discuss the use of nonstationary penalty functions to solve general nonlinear programming problems (NP ) using realvalued GAs. The nonstationary penalty is a function of the generation number; as the number of generations increases so does the penalty. Therefore, as the penalty i ..."
Abstract

Cited by 139 (7 self)
 Add to MetaCart
of these methods are reported.. 1 Introduction Constrained function optimization is an extremely important tool used in almost every facet of engineering, operations research, mathematics, and etc. Constrained optimization can be represented as a nonlinear programming problem. The general nonlinear programming
Results 1  10
of
881