LLaMEA for clustering problems

Partition to Evolve: Niching-enhanced Evolution with LLMs for Automated Algorithm Discovery

This work proposes a general LES framework that incorporates feature-assisted niche construction within abstract search spaces, enabling the seamless integration of niche-based search strategies from evolutionary computation and introduces PartEvo (Partition to Evolve), an LES method that combines niche collaborative search and advanced prompting strategies to improve algorithm discovery efficiency.

QDEvo: A Multi-Objective Quality-Diversity Framework for Automated Heuristic Design

Large Language Models (LLMs) are opening new directions for automated heuristic design (AHD), allowing evolutionary methods to create and enhance heuristics for constrained optimization problems (COPs). However, most existing approaches face the challenge of the exploration-exploitation balance, where the evolution needs to escape convergence to homogeneous populations and discover as large a heuristic landscape as possible. To address this challenge, we introduce Quality-Diversity Evolution (QDEvo), a multi-objective framework that integrates Quality-Diversity optimization with LLM-based AHD. At its core, QDEvo employs a semantic survival selection mechanism that clusters algorithms by functional similarity, then applies local Pareto competition. Evaluation on well-known COPs benchmarks and real-world problems shows that our method consistently outperforms the state-of-the-art baseline in both Hypervolume and Inverted Generational Distance metrics. These results facilitate further exploration of the algorithmic design space, while ensuring competitive solution quality and efficiency.

Claude and Gemini Design Metaheuristics: A Cooperative Multi-LLM Approach

In this entry to the GECCO 2026 Competition on LLM-Designed Metaheuristics, three large language models (GPT-5.4 Thinking, Gemini 3.1 Pro, Claude Opus 4.6) were independently prompted to design a metaheuristic for the GNBG benchmark (24 problems, hence the /24 scoring scale). Each reached a per-model ceiling (GPT 19.82, Claude 21.08, Gemini 23.29) and could not improve further on its own. Handing the best design (Gemini's) to the second-best model (Claude) for refinement produced, within four iterations, EO-BIPOP-CMA-CDR, which ranks first in the full 24-algorithm field at 23.30/24 and outperforms the source design head-to-head by three score points. A single cross-model handoff yielded more progress than five further self-refinement iterations by the source model. We argue that model diversity is a usable, low-cost resource for LLM-driven algorithm design.

Could LLMs Predict Algorithm Performance in Automated Design of Metaheuristics?

This study investigates whether the current generation of frontier Large Language Models can predict the relative performance of metaheuristic optimization algorithms from source code alone, potentially reducing the computational cost of benchmarking during automated algorithm design. We task three models (GPT 5.4 Thinking, Gemini 3.1 Pro, and Claude Opus 4.6) with predicting improvement or regression between consecutive iterations of iteratively refined differential evolution, and with ranking independently designed algorithms on standard benchmarks. Initial results are promising (88% accuracy), but accuracy collapses on a second algorithm sequence (22–44%). A controlled comparison experiment provides evidence that the models' predictions correlate with code complexity metrics rather than with actual algorithmic performance. Self-reported confidence scores are poorly calibrated across all models tested. These findings indicate that while LLM-based performance prediction is not yet a reliable substitute for benchmarking, it shows potential as a complementary pre-screening tool in iterative algorithm design, particularly in settings where code modifications follow predictable patterns.

Beyond Scalar Fitness: Behavioural Feedback for LLM-Driven Metaheuristic Evolution

LLM-Driven Configuration of Genetic Algorithms for Constraint-Rich Optimization Problems

A framework that combines large language models (LLMs) for problem understanding with a structured Biased Random-Key Genetic Algorithm (BRKGA) configurator for algorithm realization is presented, allowing users to describe optimization problems in natural language and receive executable GPU-accelerated GA implementations.