Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Journal of Global Optimization
Global Optimization of Deformable Surface Meshes Based on Genetic Algorithms
ICIAP '01 Proceedings of the 11th International Conference on Image Analysis and Processing
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
Differential Evolution: In Search of Solutions (Springer Optimization and Its Applications)
Differential Evolution: In Search of Solutions (Springer Optimization and Its Applications)
EURASIP Journal on Applied Signal Processing
Genetic approaches for topological active nets optimization
Pattern Recognition
Localisation of the optic disc by means of GA-optimised Topological Active Nets
Image and Vision Computing
Evolutionary multiobjective optimization of Topological Active Nets
Pattern Recognition Letters
Optimization of topological active nets with differential evolution
ICANNGA'11 Proceedings of the 10th international conference on Adaptive and natural computing algorithms - Volume Part I
Differential evolution optimization of 3D topological active volumes
IWANN'11 Proceedings of the 11th international conference on Artificial neural networks conference on Advances in computational intelligence - Volume Part I
DEMO: differential evolution for multiobjective optimization
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
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We performed a combination of differential evolution and a multiobjective approach for the optimization of topological active nets, a deformable model that integrates features of region-based and boundary-based segmentation techniques. As the deformation of the model is determined by the minimization of different energy components, the multiobjective methodology provides a natural solution to the minimization of the different energy components or objectives. This approach also provides a solution to the need of an experimental tuning of the weight of the energy parameters, which is also needed for each kind of image. We used a well established evolutionary multiobjective optimization algorithm, SPEA2, adapted to our application. The incorporation of the differential evolution approach in the SPEA2 algorithm allows a faster search of the Pareto Front, incorporating the main advantages of both algorithms and minimizing the designer decisions. The combined approach permits the correct segmentation of real images in the 2D and 3D domains.