SIGGRAPH '87 Proceedings of the 14th annual conference on Computer graphics and interactive techniques
Computer simulation using particles
Computer simulation using particles
Extended free-form deformation: a sculpturing tool for 3D geometric modeling
SIGGRAPH '90 Proceedings of the 17th annual conference on Computer graphics and interactive techniques
Dressing animated synthetic actors with complex deformable clothes
SIGGRAPH '92 Proceedings of the 19th annual conference on Computer graphics and interactive techniques
Predicting the drape of woven cloth using interacting particles
SIGGRAPH '94 Proceedings of the 21st annual conference on Computer graphics and interactive techniques
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Geometric modeling (2nd ed.)
Modeling inelastic deformation: viscolelasticity, plasticity, fracture
SIGGRAPH '88 Proceedings of the 15th annual conference on Computer graphics and interactive techniques
Fitting a Woven Cloth Model to a Curved Surface: Dart Insertion
IEEE Computer Graphics and Applications
Uniform Crossover in Genetic Algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
Estimating cloth simulation parameters from video
Proceedings of the 2003 ACM SIGGRAPH/Eurographics symposium on Computer animation
Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
Evolutionary Computation
Evolutionary Computation: Toward a New Philosophy of Machine Intelligence (IEEE Press Series on Computational Intelligence)
A consistent bending model for cloth simulation with corotational subdivision finite elements
Proceedings of the 2006 ACM SIGGRAPH/Eurographics symposium on Computer animation
Genetic algorithm with ant colony optimization (GA-ACO) for multiple sequence alignment
Applied Soft Computing
Short Communication: Procedural visualization of knitwear and woven cloth
Computers and Graphics
An overview of evolutionary algorithms for parameter optimization
Evolutionary Computation
Multimorphing: A tool for shape synthesis and analysis
Advances in Engineering Software
An informed genetic algorithm for the examination timetabling problem
Applied Soft Computing
To explore or to exploit: An entropy-driven approach for evolutionary algorithms
International Journal of Knowledge-based and Intelligent Engineering Systems
A survey on CAD methods in 3D garment design
Computers in Industry
Parameter control in evolutionary algorithms
IEEE Transactions on Evolutionary Computation
A note on teaching-learning-based optimization algorithm
Information Sciences: an International Journal
Exploration and exploitation in evolutionary algorithms: A survey
ACM Computing Surveys (CSUR)
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Abstract: Textile simulation models are notorious for being difficult to tune. The physically based derivations of energy functions, as mostly used for mapping the characteristics of real-world textiles on to simulation models, are labour-intensive and not guarantee satisfactory results. The extremely complex behaviour of textiles requires additional adjustment over a wide-range of parameters in order to achieve realistic real-life behaviour of the model. Furthermore, such derivations might not even be possible when dealing with mass-spring particle system-based models. Since there is no explicit correlation between the physical characteristics of textiles and the stiffnesses of springs that control a model's behaviour, this remains an unresolved issue. This paper proposes a hybrid evolutionary algorithm (EA), in order to solve this problem. The initial parameters of the model are written in individual's genes, where the number of genes is predefined for different textile types in order to limit the search-space. By mimicking the evolution processes, the EA is used to search the stability domain of the model to find a set of parameters that persuasively imitate the behaviour of a given real-world textile (e.g. silk, cotton or wool). This evaluation is based on the drape measurement, a characteristic often used when evaluating fabrics within the textile industry. The proposed EA is multi-objective, as textile drape is analysed using different quantifications. Local search is used to heuristically improve convergence towards a solution, while the efficiency of the method is demonstrated in comparison to a simple EA. To the best of our knowledge, this problem is being solved using an EA for the first time.