Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Machine vision
Computer Vision and Image Understanding
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Using an Individual Evolution Strategy for Stereovision
Genetic Programming and Evolvable Machines
Adaptation on the Evolutionary Time Scale: A Working Hypothesis and Basic Experiments
AE '97 Selected Papers from the Third European Conference on Artificial Evolution
Stereo Analysis Using Individual Evolution Strategy
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 1
Integrated Surface, Curve and Junction Inference from Sparse 3-D Data Sets
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Parisian evolution with honeybees for three-dimensional reconstruction
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Parisian camera placement for vision metrology
Pattern Recognition Letters - Special issue: Evolutionary computer vision and image understanding
Bayesian network structure learning using cooperative coevolution
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Mobile robot sensor fusion using flies
EvoWorkshops'03 Proceedings of the 2003 international conference on Applications of evolutionary computing
3D space representation by evolutive algorithms
MICAI'07 Proceedings of the artificial intelligence 6th Mexican international conference on Advances in artificial intelligence
The cooperative royal road: avoiding hitchhiking
EA'07 Proceedings of the Evolution artificielle, 8th international conference on Artificial evolution
The honeybee search algorithm for three-dimensional reconstruction
EuroGP'06 Proceedings of the 2006 international conference on Applications of Evolutionary Computing
Automated photogrammetric network design using the parisian approach
EC'05 Proceedings of the 3rd European conference on Applications of Evolutionary Computing
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The "fly algorithm" is a fast artificial evolution-based technique devised for the exploration of parameter space in pattern recognition applications. In the application described, we evolve a population which constitutes a particle-based three-dimensional representation of the scene. Each individual represents a three-dimensional point in the scene and may be fitted with optional velocity parameters. Evolution is controlled by a fitness function which contains all pixel-level calculations, and uses classical evolutionary operators (sharing, mutation, crossover). The combined individual approach and low complexity fitness function allow fast processing. Test results and an application to mobile robotics are presented.