Genetic Compression Algorithm Evolution for Large Data Sets
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Solution Overview
Problem
Current data compression methods are inefficient in reducing storage and transmission resource consumption for large data sets, as they lack an adaptive and optimized approach to select the best compression algorithms for specific data types.
Innovation Solution
The method employs genetic programming to iteratively generate, evaluate, and rank compression algorithms based on reversible matrix operations, selecting and mutating algorithms to achieve the highest degree of compression, thereby optimizing storage and bandwidth usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If traditional compression algorithms are used, then storage and transmission resources are consumed, but compression efficiency is insufficient for large data sets
Solution Approach 1:
The patent applies genetic programming to dynamically optimize compression algorithm parameters by generating mutated versions of algorithms and evaluating their performance on specific data types. This allows the system to adapt compression parameters to achieve better compression ratios and efficiency for different data characteristics.
Solution Approach 2:
The system dynamically selects and mutates compression algorithms based on the specific data type being compressed. Rather than using static compression methods, the system evolves compression algorithms through iterative evaluation and mutation to adapt to different data characteristics, improving compression efficiency for large data sets.
2Productivity
If compression algorithms are optimized for specific data types, then compression efficiency improves, but system complexity increases
Solution Approach 1:
The system uses genetic programming to automatically evolve and optimize compression algorithms without requiring manual configuration or complex decision-making logic. The algorithm self-adapts to different data types through iterative mutation and evaluation, reducing the need for complex system-level management while maintaining high compression efficiency.
Solution Approach 2:
The genetic programming framework provides a universal mechanism that can handle multiple data types and compression scenarios through a single system. The same genetic programming engine evaluates and mutates algorithms for different data types, reducing overall system complexity compared to having separate optimized algorithms for each data type.
Data Source
AI summary
Techniques for genetic programming based compression determination are described herein. An aspect includes adding a first plurality of randomly generated compression algorithms to a first set of compression algorithms. Another aspect includes determining a respective mutated version of each of the first plurality of randomly generated compression algorithms. Another aspect includes adding the determined mutated versions to the first set of compression algorithms. Another aspect includes evaluating and ranking the first set of compression algorithms based on respective achieved degrees of compression.