Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Numerical Recipes in C: The Art of Scientific Computing
Numerical Recipes in C: The Art of Scientific Computing
Simulation of Guide Wire Propagation for Minimally Invasive Vascular Interventions
MICCAI '02 Proceedings of the 5th International Conference on Medical Image Computing and Computer-Assisted Intervention-Part II
Fda -a scalable evolutionary algorithm for the optimization of additively decomposed functions
Evolutionary Computation
ViVa: the virtual vascular project
IEEE Transactions on Information Technology in Biomedicine
A real-time simulator for interventional radiology
Proceedings of the 2008 ACM symposium on Virtual reality software and technology
Artificial evolution for 3D PET reconstruction
EA'09 Proceedings of the 9th international conference on Artificial evolution
New genetic operators in the fly algorithm: application to medical PET image reconstruction
EvoApplicatons'10 Proceedings of the 2010 international conference on Applications of Evolutionary Computation - Volume Part I
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To obtain the expertise to correctly perform minimally-invasive vascular interventions thorough training is required. Training using simulation systems are increasingly becoming an accepted methodology. Recently, a minimally--invasive vascular intervention simulation (MIVIS) system has been developed. At the heart of this system lies an optimization problem to be solved repeatedly. In this paper, we investigate the advantages and disadvantages of using an evolutionary algorithm (EA) to solve the optimization problem instead of a problem--specific first--order analytical approximation algorithm. The results show that the use of the EA as optimization algorithm is favorable. A substantial reduction in time can be obtained while the RMS error associated with the simulation result differs only slightly.