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: Penalty function
"... Abstract—A stiffened laminated composite panel (1 m length × 0.5m width) was optimized for minimum weight and deflection under several constraints using genetic algorithm. Here, a significant study on the performance of a penalty function with two kinds of static and dynamic penalty factors was cond ..."
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Abstract—A stiffened laminated composite panel (1 m length × 0.5m width) was optimized for minimum weight and deflection under several constraints using genetic algorithm. Here, a significant study on the performance of a penalty function with two kinds of static and dynamic penalty factors
Penalty Functions
, 1997
"... Introduction to Constraints Most optimization problems have constraints. The solution or set of solutions which are obtained as the final result of an evolutionary search must necessarily be feasible, that is, satisfy all constraints. A taxonomy of constraints can be considered and composed of (a) ..."
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Cited by 6 (0 self)
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Introduction to Constraints Most optimization problems have constraints. The solution or set of solutions which are obtained as the final result of an evolutionary search must necessarily be feasible, that is, satisfy all constraints. A taxonomy of constraints can be considered and composed of (a) number, (b) metric, (c) criticality and (d) difficulty. A first aspect is number of constraints, ranging upwards from one. Sometimes problems with multiple objectives are reformulated with some of the objectives acting as constraints. Difficulty in satisfying constraints will increase (generally more than linearly) with the number of constraints. A second aspect of constraints is their metric, either continuous or discrete, so that a violation of the constraint can be assessed in distance from satisfaction using that metric. A third consideration is the criticality of the constraint, in terms of absolute satisfaction. A constraint is generally formulated as hard (absolute) when in fa
Nonlinear Programming without a penalty function
 Mathematical Programming
, 2000
"... In this paper the solution of nonlinear programming problems by a Sequential Quadratic Programming (SQP) trustregion algorithm is considered. The aim of the present work is to promote global convergence without the need to use a penalty function. Instead, a new concept of a "filter" is in ..."
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Cited by 252 (32 self)
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In this paper the solution of nonlinear programming problems by a Sequential Quadratic Programming (SQP) trustregion algorithm is considered. The aim of the present work is to promote global convergence without the need to use a penalty function. Instead, a new concept of a "
Restricted dissimilarity functions and penalty functions
 In Sylvie Galichet et al., editors, Proc. Eusflat/LFA 2011
, 2011
"... Abstract In this work we introduce the definition of restricted dissimilarity functions and we link it with some other notions, such as metrics. In particular, we also show how restricted dissimilarity functions can be used to build penalty functions. ..."
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Abstract In this work we introduce the definition of restricted dissimilarity functions and we link it with some other notions, such as metrics. In particular, we also show how restricted dissimilarity functions can be used to build penalty functions.
Generalized shrinkage and penalty functions
 In: IEEE Global Conference on Signal and Information Processing (2013) 3
"... All intext references underlined in blue are linked to publications on ResearchGate, letting you access and read them immediately. ..."
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Cited by 2 (0 self)
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All intext references underlined in blue are linked to publications on ResearchGate, letting you access and read them immediately.
Exponential penalty function control with queues
 Annals of Applied Probability
, 2002
"... We introduce penaltyfunctionbased admission control policies to approximately maximize the expected reward rate in a loss network. These control policies are easy to implement and perform well both in the transient period as well as in steady state. A major advantage of the penalty approach is tha ..."
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We introduce penaltyfunctionbased admission control policies to approximately maximize the expected reward rate in a loss network. These control policies are easy to implement and perform well both in the transient period as well as in steady state. A major advantage of the penalty approach
A Family of Penalty Functions for Structured
"... We study the problem of learning a sparse linear regression vector under additional conditions on the structure of its sparsity pattern. We present a family of convex penalty functions, which encode this prior knowledge by means of a set of constraints on the absolute values of the regression coeffi ..."
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We study the problem of learning a sparse linear regression vector under additional conditions on the structure of its sparsity pattern. We present a family of convex penalty functions, which encode this prior knowledge by means of a set of constraints on the absolute values of the regression
The Discounted Penalty Function and the . . . Risk Model
, 2009
"... In this thesis, we study the expected discounted penalty function and the total dividend payments in a risk model with a multithreshold dividend strategy, where the claim arrivals are modeled by a Markovian arrival process (MAP) and the claim amounts are correlated with the interclaim times. Syst ..."
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In this thesis, we study the expected discounted penalty function and the total dividend payments in a risk model with a multithreshold dividend strategy, where the claim arrivals are modeled by a Markovian arrival process (MAP) and the claim amounts are correlated with the interclaim times
Penalty Functions for Genetic Programming Algorithms
"... Abstract. Very often symbolic regression, as addressed in Genetic Programming (GP), is equivalent to approximate interpolation. This means that, in general, GP algorithms try to fit the sample as better as possible but no notion of generalization error is considered. As a consequence, overfitting, c ..."
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methodological framework for complexity control in Genetic Programming even when its technical results seems not be directly applicable. As main practical advantage, precise penalty functions founded on the notion of generalization error are proposed for evolving GPtrees.
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