Target detection in SAR imagery by genetic programming
Advances in Engineering Software
Coevolving functions in genetic programming
Journal of Systems Architecture: the EUROMICRO Journal - Special issue on evolutionary computing
Genetic Programming for Feature Discovery and Image Discrimination
Proceedings of the 5th International Conference on Genetic Algorithms
A Cooperative Coevolutionary Approach to Function Optimization
PPSN III Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: Parallel Problem Solving from Nature
Evolution of Ship Detectors for Satellite SAR Imagery
Proceedings of the Second European Workshop on Genetic Programming
GECCO '96 Proceedings of the 1st annual conference on Genetic and evolutionary computation
Morphological algorithm design for binary images using genetic programming
Genetic Programming and Evolvable Machines
Heterogeneous cooperative coevolution: strategies of integration between GP and GA
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Feature extraction and classification by genetic programming
ICVS'08 Proceedings of the 6th international conference on Computer vision systems
New crossover operators in linear genetic programming for multiclass object classification
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Genetic Programming and Evolvable Machines
The automatic generation of mutation operators for genetic algorithms
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
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In previous work, we showed how cooperative coevolution could be used to evolve both the feature construction stage and the classification stage of an object detection algorithm. Evolving both stages simultaneously allows highly accurate solutions to be created while needing only a fraction of the number of features extracting as in generic approaches. Scalability issues in the previous system have motivated the introduction of a multi-stage approach which has been shown in the literature to provide large reductions in computational requirements. In this work we show how using the idea of coevolutionary feature extraction in conjunction with this multi-stage approach can reduce the computational requirements by at least two orders of magnitude, allowing the impressive performance gains of this technique to be readily applied to many real world problems.