This paper presents the potential of genetic programming (GP), an evolutionary computing algorithm, for reducing or eliminating significant second-order linear model LOF by automatically generating appropriate transfo...
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This paper presents the potential of genetic programming (GP), an evolutionary computing algorithm, for reducing or eliminating significant second-order linear model LOF by automatically generating appropriate transformations. A case study in an industrial setting at The Dow Chemical Company will be presented to illustrate this methodology. Lack of fit, transformations, linear regression.
The main goal of this flight control system is to achieve good performance with acceptable flying quality within the specified flight envelope while ensuring robustness for model variations, such as mass variation due...
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Sparse large-scale multi-objective optimization problems(SLMOPs)are common in science and ***,the large-scale problem represents the high dimensionality of the decision space,requiring algorithms to traverse vast expa...
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Sparse large-scale multi-objective optimization problems(SLMOPs)are common in science and ***,the large-scale problem represents the high dimensionality of the decision space,requiring algorithms to traverse vast expanse with limited computational ***,in the context of sparse,most variables in Pareto optimal solutions are zero,making it difficult for algorithms to identify non-zero variables *** paper is dedicated to addressing the challenges posed by *** start,we introduce innovative objective functions customized to mine maximum and minimum candidate *** substantial enhancement dramatically improves the efficacy of frequent pattern *** this way,selecting candidate sets is no longer based on the quantity of nonzero variables they contain but on a higher proportion of nonzero variables within specific ***,we unveil a novel approach to association rule mining,which delves into the intricate relationships between non-zero *** novel methodology aids in identifying sparse distributions that can potentially expedite reductions in the objective function *** extensively tested our algorithm across eight benchmark problems and four real-world *** results demonstrate that our approach achieves competitive solutions across various challenges.
Over the last two decades, evolutionary Computation (EC) has shown tremendous success for solving complex real-world problems. Although the great success for EC was first recognized in the 1980s, the researchers in ot...
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Aircraft flight control design is a multivariable control problem with multiple sensors and multiple actuators where various strict requirements from multiple disciplines have to be satisfied. In this paper a method b...
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The prediction of electron distributions in semiconductor devices is compulsory for the design of modern computer chips. In spite of increasing computation facilities the calculation of electron distributions at high ...
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This paper reports work investigating various evolutionary approaches to vertex cover (VC), a well-known NP-Hard optimization problem. Central to each of the algorithms is a novel encoding scheme for VC and related pr...
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A graph is edge-biconnected if it requires the removal of at least two edges to disconnect it. Assume that we have weighted graph that is not biconnected, and an additional set of augmentation edges. The (NP-hard) edg...
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A graph is edge-biconnected if it requires the removal of at least two edges to disconnect it. Assume that we have weighted graph that is not biconnected, and an additional set of augmentation edges. The (NP-hard) edge biconnectivity augmentation problem is to select a minimal subset of the augmentation edges, whose inclusion will cause the graph to be biconnected. This paper explores the application of particle swarm optimization and genetic algorithms for this problem.
This paper investigates groundwater system characterization problem, in this inverse problem the contaminant signals at monitoring wells are recorded to recreate the pollution profiles. In this study, simulation-optim...
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作者：
Toscano, R.Lyonnet, P.Université de Lyon
Laboratoire de Tribologie et de Dynamique des Systémes CNRS UMR5513 ECL/ENISE 58 rue Jean Parot 42023 Saint-Etienne Cedex 2 France
In this paper we introduce an extension of standard geometric programming (GP) problems which we call quasi geometric programming (QGP) problems. The consideration of this particular kind of nonlinear and possibly non...
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ISBN:
(纸本)9789898425317
In this paper we introduce an extension of standard geometric programming (GP) problems which we call quasi geometric programming (QGP) problems. The consideration of this particular kind of nonlinear and possibly non smooth optimization problem is motivated by the fact that many engineering problems can be formulated as a QGP. However, solving a QGP remains a difficult task due to its intrinsic non-convex nature. This is why we investigate the possibility of using evolutionary algorithms (EA) for solving a QGP problem. The main idea developed in this paper is to combine evolutionary algorithms with interior point method for efficiently solving QGP problems. An interesting feature of the proposed approach is that it does not need to develop specific program solver and works well with any existing EA and available solver able to solve conventional GP. Some considerations on the robustness issue are also presented. Numerical experiments are used to validate the proposed method.
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