Evolutionary lossless compression with GP-ZIP*
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Connection Science - Evolutionary Learning and Optimisation
A novel artistic image generation technique: making relief effects through evolution
ISICA'07 Proceedings of the 2nd international conference on Advances in computation and intelligence
Evolution of human-competitive lossless compression algorithms with GP-zip2
Genetic Programming and Evolvable Machines
Towards intrinsic evolvable hardware for predictive lossless image compression
SEAL'06 Proceedings of the 6th international conference on Simulated Evolution And Learning
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By introducing the optimal linear predictive code technic into the dynamic issue of lossess image compression, this paper presented a less complexity fitness function for Genetic Programming engine, which can reduce the cost of computational time in each evaluation for individual greatly, and can also provide further benefit with the scalability issue. To make the speed of large image compression faster in condition of not increasing the cost of computational resource and time, evaluating mechanism in the field of machine learning was used to help Genetic Programming, and the scalability issue was mapped to the task of making the approach accuracy best from lower speed sampling to higher speed sampling in the field of signal processing. In experiments for compressing large images, the cost of computational time was reduced evidently and efficiently.