Delta encoding

Delta encoding

An Empirical Evaluation of Genetic Algorithms on Noisy Objective Functions

Published in Sankar K. Pal, Paul P. Wang, Genetic Algorithms for Pattern Recognition, 2017

Keith Mathias, Darrell Whitley, Anthony Kusuma, Christof Stork

The hybrid delta coding algorithm provides the same separation of effort between the local and genetic search components as the other staged algorithms. It also saves computational effort by applying the waveform steepest ascent algorithm to a limited number of models. The delta coding algorithm also provides some additional advantages including a reduced population size (representing an additional time savings), reduced search space, and the exploration of multiple mappings of the search space over the course of the search.

A feature-based intelligent deduplication compression system with extreme resemblance detection

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Journal Information

Published in Connection Science, 2021

Xiaotong Wu, Jiaquan Gao, Genlin Ji, Taotao Wu, Yuan Tian, Najla Al-Nabhan

Delta compression is used to compress two similar files or chunks. For a target chunk, its source chunk is a base, which is used as a reference of delta compression. The data after compression is the difference between chunks. In FIDCS-ERD, it implements delta encoding based on Xdelta (MacDonald, 2000), which is one of the optimal approaches to compress highly similar data streams. That is, Xdelta introduces a Copy/Insert algorithm, which utilises a string matching technique to first find matching offsets in SC and TC and then generate a set of Copy instructions for every matching range and Insert instructions to cover the unmatched regions. Xdelta is an approximate implementation of the greedy algorithm based on the hash techniques. It satisfies the linear time and space complexity at the cost of sub-optimal compression.

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An Empirical Evaluation of Genetic Algorithms on Noisy Objective Functions

A feature-based intelligent deduplication compression system with extreme resemblance detection