Design of Evolutionary Algorithms and Applications in Surface Reconstruction

Beielstein, Thomas; Mehnen, Jörn; Schönemann, Lutz; Schwefel, Hans–Paul; Surmann, Tobias; Weinert, Klaus; Wiesmann, Dirk

doi:10.1007/978-3-662-05609-7_6

Cited by 6 publications

(7 citation statements)

References 27 publications

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“…Thus, the position vector su,v can be expressed as linear-weighted combination of the control points. In an optimal approximation ∀u, v : suv = puv (2) holds. The basic functions and the weights form a coefficient matrix C, which is applied to the vector x of control points to interpolate the vector of corresponding scan points s. In this vein, a linear system of equations Cx = s is established.…”

Section: On Objective Functionsmentioning

confidence: 99%

“…Thus, no exact solution exists. SVD obtains a solution x that minimises the squared distance between the corresponding points in equation (2). Hence, x gives a discrete approximation of the optimal control point coordinates with regard to the chosen corresponding points.…”

Section: On Objective Functionsmentioning

confidence: 99%

“…They report runtimes up to 24 hours for the optimisation of a NURBS to model a simple structure due to expensive function evaluations and the high dimensionality of the decision space. Thus, Beielstein et al [2] divide the process of optimisation in different steps where only the z-coordinate of the control points is modified in the beginning. Only afterwards, the other parameters become the subjects of optimisation.…”

Section: Introductionmentioning

confidence: 99%

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On the design of optimisers for surface reconstruction

Wagner

Michelitsch

Sacharow

2007

Proceedings of the 9th Annual Conference on Genetic and Evolutionary Computation

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In many industrial applications the need for an efficient and high-quality reconstruction of free-form surfaces does exist. Surface Reconstruction -the generation of CAD models from physical objects -has become an independent area of research. The supplementary modification and the automated manufacturing of workpieces represent typical fields of application. Small tolerances in the desired properties result in a very high number of scan points needed. Thus, modern approaches have to be capable of processing, analysing and modelling these amounts of data.There are several studies that use evolutionary algorithms (EA) for surface reconstruction tasks. Until now, these studies only describe the general ability of EA to successfully optimise surfaces. Aspects like runtime as well as comparability to other optimisation techniques have not been considered. Since these aspects are of great importance for integration in applicable software tools, in this paper the ability of a state-of-the-art multi-objective EA to be successfully integrated in surface reconstruction software is analysed. Major drawbacks are disclosed and necessary add-on modules are presented.

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Section: On Objective Functionsmentioning

confidence: 99%

Section: On Objective Functionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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On the design of optimisers for surface reconstruction

Wagner

Michelitsch

Sacharow

2007

Proceedings of the 9th Annual Conference on Genetic and Evolutionary Computation

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“…The design and analysis of representations and their corresponding genetic operators is the normal course of life in the field of evolutionary computation [2]. Often an adept combination of all components determines the system's success or failure.…”

Section: Introductionmentioning

confidence: 99%

Inside a predator-prey model for multi-objective optimization

Grimme

Schmitt

2006

Proceedings of the 8th Annual Conference on Genetic and Evolutionary Computation

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In this article, new variation operators for evolutionary multiobjective algorithms (EMOA) are proposed. On the basis of a predator-prey model theoretical considerations as well as empirical results lead to the development of a new recombination operator, which improves the approximation of the set of efficient solutions significantly. Furtheron, it is shown that applying speciation to the analysed model makes it possible to handle even more complex problems.

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“…One main research topic in evolutionary computation is the design of problem-specific evolutionary algorithms (Beielstein et al, 2003). The aim is to systematize the design of evolutionary algorithms for problems with nonstandard representations.…”

Section: Methodsmentioning

confidence: 99%

Optimizing Door Assignment in LTL-Terminals by Evolutionary Multiobjective Algorithms

Bartz-Beielstein

Chmielewski²,

Janas³

et al.

2006 IEEE International Conference on Evolutionary Computation

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Abstract-In less-than-truckload terminals arriving trucks have to be allocated to a gate and to a time slot for unloading. The allocation to a specific gate results in different transportation volumes for the forklift trucks inside of the terminal, depending on the destinations of the truck's loads. While minimizing these transports the time for trucks waiting to be ordered to a gate also has to be minimized. For the first time this problem has been tackled as a 2-objective optimization problem and was solved by an (1+1)-evolution strategy. We developed a model which is derived from real freight forwarder's data and represents a small company's terminal on an average workday.

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Design of Evolutionary Algorithms and Applications in Surface Reconstruction

Cited by 6 publications

References 27 publications

On the design of optimisers for surface reconstruction

On the design of optimisers for surface reconstruction

Inside a predator-prey model for multi-objective optimization

Optimizing Door Assignment in LTL-Terminals by Evolutionary Multiobjective Algorithms

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