Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Image classification: an evolutionary approach
Pattern Recognition Letters
Genetic Programming for Feature Discovery and Image Discrimination
Proceedings of the 5th International Conference on Genetic Algorithms
Genetic Programming for Feature Detection and Image Segmentation
Selected Papers from AISB Workshop on Evolutionary Computing
Strongly typed genetic programming
Evolutionary Computation
Improved directed acyclic graph evaluation and the combine operator in genetic programming
GECCO '96 Proceedings of the 1st 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
Complexity and cartesian genetic programming
EuroGP'06 Proceedings of the 9th European conference on Genetic Programming
Parallel linear genetic programming for multi-class classification
Genetic Programming and Evolvable Machines
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Genetic programming (GP) has long been known as a computationally expensive optimisation technique. When evolving imaging operations, the processing time increases dramatically. This work describes a system using a caching mechanism which reduces the number of evaluations needed by up to 66 percent, counteracting the effects of increasing tree size. This results in a decrease in elapsed time of up to 52 percent. A cost threshold is introduced which can guarantee a speed increase. This caching technique allows GP to be feasibly applied to problems in computer vision and image processing. The trade-offs involved in caching are analysed, and the use of the technique on a previously time consuming medical segmentation problem is shown.