Automated Static Library Optimization via Hybrid Symbolic Regression and Evolutionary Algorithm
Year: 2025
Type: article
Abstract: **Abstract:** This paper presents a novel framework for automated optimization of static libraries, leveraging a hybrid approach combining symbolic regression and an evolutionary algorithm. Current static library optimization techniques often rely on hand-tuned heuristics or simplified profiling, failing to fully exploit the vast parameter space and intricate interdependencies within complex library code. Our framework, termed "EvoSymbolLibOpt," dynamically generates and evaluates optimized versions of static libraries through exploration of symbolic representations of performance bottlenecks and iterative refinement via an evolutionary algorithm. This approach achieves a significant reduction in execution time and memory footprint compared to conventional optimization methods while maintaining functional equivalence. --- *This document is a part of the Freederia Research Archive. Explore our complete collection of advanced research at [en.freederia.com](https://en.freederia.com), or visit our main portal at [freederia.com](https://freederia.com) to learn more about our mission and other initiatives.*
Source: Figshare
Authors: KYUNGJUN, LIM, Researcher, Freederia AI
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W6940334249
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