A Method for Registration of 3-D Shapes
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part II
The NURBS book
Numerical Recipes in C++: the art of scientific computing
Numerical Recipes in C++: the art of scientific computing
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Combining convergence and diversity in evolutionary multiobjective optimization
Evolutionary Computation
Completely Derandomized Self-Adaptation in Evolution Strategies
Evolutionary Computation
Improved algorithms for the projection of points on NURBS curves and surfaces
Computer Aided Geometric Design
Experimental Research in Evolutionary Computation: The New Experimentalism (Natural Computing Series)
Capabilities of EMOA to detect and preserve equivalent pareto subsets
EMO'07 Proceedings of the 4th international conference on Evolutionary multi-criterion optimization
Steady-state selection and efficient covariance matrix update in the multi-objective CMA-ES
EMO'07 Proceedings of the 4th international conference on Evolutionary multi-criterion optimization
Pareto-, aggregation-, and indicator-based methods in many-objective optimization
EMO'07 Proceedings of the 4th international conference on Evolutionary multi-criterion optimization
An EMO algorithm using the hypervolume measure as selection criterion
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
Performance assessment of multiobjective optimizers: an analysis and review
IEEE Transactions on Evolutionary Computation
EMO '09 Proceedings of the 5th International Conference on Evolutionary Multi-Criterion Optimization
Information Sciences: an International Journal
Information Sciences: an International Journal
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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.