Unified theories of cognition
Evolution and Optimum Seeking: The Sixth Generation
Evolution and Optimum Seeking: The Sixth Generation
An Evolutionary Algorithm for Integer Programming
PPSN III Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: Parallel Problem Solving from Nature
Mixed-Integer Evolution Strategies with Dynamic Niching
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
An evolutionary approach for achieving scalability with general regression neural networks
Natural Computing: an international journal
Towards Dynamic Fitness Based Partitioning for IntraVascular UltraSound Image Analysis
Proceedings of the 2007 EvoWorkshops 2007 on EvoCoMnet, EvoFIN, EvoIASP,EvoINTERACTION, EvoMUSART, EvoSTOC and EvoTransLog: Applications of Evolutionary Computing
Optimizing computed tomographic angiography image segmentation using fitness based partitioning
Evo'08 Proceedings of the 2008 conference on Applications of evolutionary computing
Mixed integer evolution strategies for parameter optimization
Evolutionary Computation
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In this paper we compare Mixed-Integer Evolution Strategies (MI-ES)and standard Evolution Strategies (ES)when applied to find optimal solutions for artificial test problems and medical image processing problems. MI-ES are special instantiations of standard ES that can solve optimization problems with different objective variable types (continuous, integer, and nominal discrete). Artificial test problems are generated with a mixed-integer test generator.The practical image processing problem iss the detection of the lumen boundary in IntraVascular UltraSound (IVUS)images. Based on the experimental results, it is shown that MI-ES generally perform better than standard ES on both artifical and practical image processing problems. Moreover it is shown that MI-ES can effectively improve the parameters settings for the IVUS lumen detection algorithm.