EASE for LLM-Designed Evolutionary Algorithms: A GECCO 2026 Competition Entry

Abstract

This paper presents the competition entry on LLM-designed evolutionary algorithms for the GNBG-generated benchmark suite. We use EASE (Effortless Algorithmic Solution Evolution), a modular framework that prompts a large language model to generate complete optimizers, evaluates them under the competition protocol, and feeds performance summaries together with improvement-oriented analysis back into subsequent iterations. The automated process produced ten valid candidate algorithms. The best generated solver was a hybrid adaptive Differential Evolution method combining L-SHADE-style parameter adaptation, archive-based diversity preservation, covariance-inspired variation, and budget-aware local refinement. The results show that iterative LLM-guided refinement can produce effective, benchmark-specific optimizers.