Jul 2026· GECCO Companion· pp. 9-10· 0 citations· 5 references
Computer Science
TL;DR
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 shows that iterative LLM-guided refinement can produce effective, benchmark-specific optimizers.
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.
Experimental results demonstrate that LLMDE achieves competitive performance on the benchmark suite while continuously generating high-quality solutions for complex constrained optimization tasks, and successfully demonstrate the viability of embedding LLMs within metaheuristics, paving a promising path toward the desi...
Rong-Mu Chai, V. Snášel, Xiao-Peng Wang et al.· 0 citations
LLM-guided evolutionary methods, such as AlphaEvolve, have emerged as powerful approaches for challenging computational optimization problems, such as circle packing. However, prior work typically optimizes performance gain over a fixed number of iterations. We argue that practical optimization should maximize gain per...
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M. Efendiev, Sviataslau Redzkin, Olga I. Serebryakova· 2026 International Russian A...· 0 citations
Large language models (LLMs) are increasingly being employed as variation operators in metaheuristics, generating or modifying candidate solutions, heuristics, or programs inside iterative search loops. This shift reframes variation as a model call conditioned on different types of information. We introduce an operator...
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Discovery Loop is a lightweight system that uses a large language model (LLM) to iteratively evolve optimization algorithms to improve the best known solutions for 10 values of N in the range 101-114, with gains of 2.4%-5.4% over prior records.
A solver-informed diagnosis mechanism that exploits fine-grained solver statistics as verbal gradients for targeted refinement and a structured memory abstracts prior experience into reusable modeling strategies, avoiding redundant exploration while enabling zero-shot transfer to unseen problems and bootstrapping small...
Haofeng Yuan, Jianing Peng, Jieyi Bi et al.· 0 citations
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