It is found that none of the LLaMEA generated algorithms outperformed KMeans++, and prompting strategy affects the structural diversity of generated algorithms, with exemplar-injection producing the broadest range of algorithm families.
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 s...
Qinglong Hu, Qingfu Zhang· Neural Information Processin...· 10 citations· ⚡3
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 nee...
Nam Do Khanh, Nhat Nguyen Tran Minh, Dat Pham Vu Tuan et al.· Proceedings of the Genetic a...· 1 citation
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.
Michal Pluháček, Paweł Kolendo, Krzysztof Tylka-Suleja et al.· GECCO Companion· 0 citations
It is found that neutral framing of behavioural features, reporting the feature value without prescriptive advice, consistently outperforms the prescriptive variants, and that prescriptive feedback steers the median feature value in the advised direction in only 37% of cases despite empirically grounded advice.
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 implementatio...
Harishjitu Seesandrn, M. Sodhi, Resit Sendag· GECCO Companion· 0 citations
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