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Full-Text Articles in Computer Engineering
Evolutionary Methodology For Optimization Of Image Transforms Subject To Quantization Noise, Michael Ray Peterson
Evolutionary Methodology For Optimization Of Image Transforms Subject To Quantization Noise, Michael Ray Peterson
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Lossy image compression algorithms sacrifice perfect imagereconstruction in favor of decreased storage requirements. Modelossy compression schemes, such as JPEG2000, rely upon the discrete wavelet transform (DWT) to achieve high levels of compression while minimizing the loss of information for image reconstruction. Some compression applications require higher levels of compression than those achieved through application of the DWT and entropy coding. In such lossy systems, quantization provides high compression rates at the cost of increased distortion. Unfortunately, as the amount of quantization increases, the performance of the DWT for accurate image reconstruction deteriorates. Previous research demonstrates that a genetic algorithm can …
An Analysis Of Neutral Drift's Effect On The Evolution Of A Ctrnn Locomotion Controller With Noisy Fitness Evaluation, Gregory Robert Kramer
An Analysis Of Neutral Drift's Effect On The Evolution Of A Ctrnn Locomotion Controller With Noisy Fitness Evaluation, Gregory Robert Kramer
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This dissertation focuses on the evolution of Continuous Time Recurrent Neural Networks (CTRNNs) as controllers for control systems. Existing research suggests that the process of neutral drift can greatly benefit evolution for problems whose fitness landscapes contain large-scale neutral networks. CTRNNs are known to be highly degenerate, providing a possible source of large-scale landscape neutrality, and existing research suggests that neutral drift benefits the evolution of simple CTRNNs. However, there has been no in-depth examination of the effects of neutral drift on complex CTRNN controllers, especially in the presence of noisy fitness evaluation. To address this problem, this dissertation presents …
Pattern Recognition Via Machine Learning With Genetic Decision-Programming, Carl C. Hoff
Pattern Recognition Via Machine Learning With Genetic Decision-Programming, Carl C. Hoff
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In the intersection of pattern recognition, machine learning, and evolutionary computation is a new search technique by which computers might program themselves. That technique is called genetic decision-programming. A computer can gain the ability to distinguish among the things that it needs to recognize by using genetic decision-programming for pattern discovery and concept learning. Those patterns and concepts can be easily encoded in the spines of a decision program (tree or diagram). A spine consists of two parts: (1) the test-outcome pairs along a path from the program's root to any of its leaves and (2) the conclusion in that …