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Hyperheuristics and Classifier Systems for . . .
, 2005
"... This paper presents a method for combining concepts of Hyperheuristics and Learning Classifier Systems for solving 2D Cutting Stock Problems. The idea behind Hyperheuristics is to discover some combination of straightforward heuristics to solve a wide range of problems. To be worthwhile, such combi ..."
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This paper presents a method for combining concepts of Hyperheuristics and Learning Classifier Systems for solving 2D Cutting Stock Problems. The idea behind Hyperheuristics is to discover some combination of straightforward heuristics to solve a wide range of problems. To be worthwhile
A Survey of Hyperheuristics
, 2009
"... In an attempt to ensure goodquality printouts of our technical reports, from the supplied PDF files, we process to PDF using Acrobat Distiller. We encourage our authors to use outline fonts coupled with embedding of the used subset of all fonts (in either Truetype or Type 1 formats) except for the ..."
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Modern bitmapped fonts. Note that the Y&Y font subsets are embedded under a site license issued by Y&Y Inc. For further details of site licensing and purchase of these fonts visit
HyperHeuristics: An Emerging Direction In Modern Search Technology
, 2003
"... This chapter introduces and overviews an emerging methodology in search and optimisation. One of the key aims of these new approaches, which have been termed hyperheuristics, is to raise the level of generality at which optimisation systems can operate. An objective is that hyperheuristics will le ..."
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Cited by 119 (41 self)
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or algorithm in a given situation. Of course, a hyperheuristic can be (often is) a (meta)heuristic and it can operate on (meta)heuristics. In a certain sense, a hyperheuristic works at a higher level when compared with the typical application of metaheuristics to optimisation problems i.e. a hyperheuristic
A Limited Memory Algorithm for Bound Constrained Optimization
 SIAM Journal on Scientific Computing
, 1994
"... An algorithm for solving large nonlinear optimization problems with simple bounds is described. ..."
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Cited by 557 (9 self)
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An algorithm for solving large nonlinear optimization problems with simple bounds is described.
Multiobjective Optimization Using Nondominated Sorting in Genetic Algorithms
 Evolutionary Computation
, 1994
"... In trying to solve multiobjective optimization problems, many traditional methods scalarize the objective vector into a single objective. In those cases, the obtained solution is highly sensitive to the weight vector used in the scalarization process and demands the user to have knowledge about t ..."
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Cited by 524 (4 self)
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In trying to solve multiobjective optimization problems, many traditional methods scalarize the objective vector into a single objective. In those cases, the obtained solution is highly sensitive to the weight vector used in the scalarization process and demands the user to have knowledge about
The Vocabulary Problem in HumanSystem Communication
 COMMUNICATIONS OF THE ACM
, 1987
"... In almost all computer applications, users must enter correct words for the desired objects or actions. For success without extensive training, or in firsttries for new targets, the system must recognize terms that will be chosen spontaneously. We studied spontaneous word choice for objects in five ..."
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Cited by 551 (8 self)
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. For example, the popular approach in which access is via one designer's favorite single word will result in 8090 percent failure rates in many common situations. An optimal strategy, unlimited aliasing, is derived and shown to be capable of severalfold improvements.
A training algorithm for optimal margin classifiers
 PROCEEDINGS OF THE 5TH ANNUAL ACM WORKSHOP ON COMPUTATIONAL LEARNING THEORY
, 1992
"... A training algorithm that maximizes the margin between the training patterns and the decision boundary is presented. The technique is applicable to a wide variety of classifiaction functions, including Perceptrons, polynomials, and Radial Basis Functions. The effective number of parameters is adjust ..."
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Cited by 1848 (44 self)
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is adjusted automatically to match the complexity of the problem. The solution is expressed as a linear combination of supporting patterns. These are the subset of training patterns that are closest to the decision boundary. Bounds on the generalization performance based on the leaveoneout method and the VC
Particle swarm optimization
, 1995
"... eberhart @ engr.iupui.edu A concept for the optimization of nonlinear functions using particle swarm methodology is introduced. The evolution of several paradigms is outlined, and an implementation of one of the paradigms is discussed. Benchmark testing of the paradigm is described, and applications ..."
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Cited by 3535 (22 self)
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eberhart @ engr.iupui.edu A concept for the optimization of nonlinear functions using particle swarm methodology is introduced. The evolution of several paradigms is outlined, and an implementation of one of the paradigms is discussed. Benchmark testing of the paradigm is described
Genetic Algorithms for Multiobjective Optimization: Formulation, Discussion and Generalization
, 1993
"... The paper describes a rankbased fitness assignment method for Multiple Objective Genetic Algorithms (MOGAs). Conventional niche formation methods are extended to this class of multimodal problems and theory for setting the niche size is presented. The fitness assignment method is then modified to a ..."
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Cited by 610 (15 self)
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The paper describes a rankbased fitness assignment method for Multiple Objective Genetic Algorithms (MOGAs). Conventional niche formation methods are extended to this class of multimodal problems and theory for setting the niche size is presented. The fitness assignment method is then modified
SNOPT: An SQP Algorithm For LargeScale Constrained Optimization
, 2002
"... Sequential quadratic programming (SQP) methods have proved highly effective for solving constrained optimization problems with smooth nonlinear functions in the objective and constraints. Here we consider problems with general inequality constraints (linear and nonlinear). We assume that first deriv ..."
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Cited by 582 (23 self)
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Sequential quadratic programming (SQP) methods have proved highly effective for solving constrained optimization problems with smooth nonlinear functions in the objective and constraints. Here we consider problems with general inequality constraints (linear and nonlinear). We assume that first
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