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Simulating Evolution Algorithm and Deep Reinforcement Learning Hybrid Framework

Jincheng Zhang
Aug 2026 · Zenodo (CERN European Organization for Nuclear Research)
Advanced Multi-Objective Optimization Algorithms Metaheuristic Optimization Algorithms Research

Abstract

This paper proposes a novel hybrid framework integrating the Simulated Evolution Algorithm (SEA) and Deep Reinforcement Learning (DRL) to tackle complex optimization problems. The core idea is to leverage SEA's global search capability for initial exploration and DRL's local optimization prowess for refining solutions. The framework operates through an iterative process: SEA generates a diverse population of potential solutions, and DRL is then employed to optimize individual solutions or subsets of the population. Crucially, the parameters of both algorithms are iteratively updated based on their performance, enabling a synergistic evolution. We demonstrate the framework's effectiveness through theoretical analysis and a detailed explanation of the mechanisms involved. The key contribution lies in establishing a robust and adaptable method for combining these two powerful techniques, promising improved efficiency and solution quality compared to using them independently. This work provides a foundational approach for future research exploring the synergy between evolutionary and reinforcement learning methods.

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