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#reinforcement learning Open access

Dynamic Symbolic Regression with Evolutionary Reinforcement Learning

Jincheng Zhang
Aug 2026 · Zenodo (CERN European Organization for Nuclear Research)
Evolutionary Algorithms and Applications

Abstract

This paper presents a novel approach to symbolic regression, termed Dynamic Symbolic Regression with Evolutionary Reinforcement Learning (DSR-ERL). The core idea is to leverage reinforcement learning (RL) to dynamically control an evolutionary algorithm, specifically optimizing the mutation and crossover operators within the symbolic regression process. Traditional symbolic regression methods often suffer from issues like premature convergence and inefficient exploration of the expression space. Our system addresses these challenges by allowing an RL agent to learn a policy that adapts the evolutionary strategy based on the quality of the generated symbolic expressions. The agent observes the fitness (evaluation) of the expression and adjusts the evolutionary operators to favor solutions with higher fitness. We demonstrate the effectiveness of DSR-ERL through a theoretical analysis and a conceptual framework, outlining the key components and their interactions. The system offers the potential to discover more accurate and efficient mathematical models from data, particularly in complex domains. The key innovation lies in the adaptive control provided by the RL agent, leading to a dynamic and potentially superior symbolic regression process. We provide a detailed description of the system's architecture, including the RL agent, the evolutionary algorithm, and the interface between them. Furthermore, we discuss the challenges and future directions of this research.

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