On Program Self-Optimization Based on Information Entropy

Abstract

This paper explores a novel approach to program self-optimization, termed "Entropy-Driven Program Self-Optimization (EDPSO)." The core concept revolves around a program's ability to monitor and adapt its own execution based on the inherent information entropy within its processes. The system employs an "entropy-aware" module to continuously measure the entropy during program execution. This entropy value then acts as a feedback signal, guiding the adjustment of the program's code structure. Genetic algorithms and/or reinforcement learning are utilized to optimize the code, driven by the dynamic entropy feedback. The key innovation lies in the program's intrinsic understanding of its own performance, shifting away from solely relying on external metrics. The proposed EDPSO framework presents a potentially powerful methodology for enhancing program efficiency and adaptability, particularly in complex and evolving environments. This paper details the architecture, the entropy measurement methodology, and the optimization algorithms utilized within EDPSO, outlining a pathway towards truly self-optimizing software.