Evolutionary Algorithm Storage Optimization via Survivor Generation Prioritization
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Solution Overview
Problem
Evolutionary algorithms are computationally expensive and resource-intensive, requiring many generations and iterations to converge to a solution, which can lead to high processor time and storage space usage, necessitating optimized resource management.
Innovation Solution
Implementing algorithm-aware tiering and caching to optimize storage usage by analyzing the resource usage patterns of instance generations, prioritizing survivor generations, and adjusting storage tiers based on their fitness and lineage, thereby optimizing storage and resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If evolutionary algorithms execute many generations and iterations to converge to a solution, then solution accuracy is improved, but storage space usage increases
Solution Approach 1:
The patent applies local quality by differentiating storage priorities among different generations. Survivor generations (fit members) receive high-priority storage allocation while non-survivor generations (unfit members) receive low-priority or no storage allocation. This selective storage strategy maintains solution accuracy by preserving essential genetic information while reducing overall storage space usage by eliminating redundant data from unsuccessful generations.
2Measurement precision
If evolutionary algorithms execute many generations and iterations to converge to a solution, then solution accuracy is improved, but processor time usage increases
Solution Approach 1:
The patent extracts and eliminates redundant computational work by identifying and removing non-survivor generations from further processing. By selecting only fit members as survivors that contribute to subsequent generations, the algorithm avoids re-processing unsuccessful candidate solutions, thereby reducing processor time usage while maintaining the ability to converge to accurate solutions through preserved survivor genetic information.
3Loss of information
If all instance generations are stored with equal priority, then data completeness is maintained, but storage efficiency decreases
Solution Approach 1:
The patent implements local quality by assigning different storage priorities to different generations based on their fitness characteristics. Survivor generations are assigned high storage priority to ensure data completeness for successful solutions, while non-survivor generations are assigned low storage priority or discarded. This differential storage strategy maintains essential genetic information for convergence while significantly improving storage efficiency by eliminating redundant data from unsuccessful generations.
Data Source
AI summary
A method, computer program, and computer system are provided for optimizing storage usage of evolutionary algorithms. One or more instance generations associated with an evolutionary algorithm are executed. Data corresponding to inputs and outputs associated with each of the executed instance generations is identified. One or more survivor generations are determined from among the instance generations based on analyzing a fitness associated with the instance generations. The data corresponding to inputs and outputs associated with the determined survivor generations is prioritized.