Fitness Landscape of Large Language Model-Assisted Automated Algorithm Search

• URL: http://arxiv.org/abs/2504.19636v2
• Date: Thu, 01 May 2025 08:33:32 GMT
• Title: Fitness Landscape of Large Language Model-Assisted Automated Algorithm Search
• Authors: Fei Liu, Qingfu Zhang, Xialiang Tong, Kun Mao, Mingxuan Yuan,
• Abstract summary: We show and analyze the fitness landscape of Large Language Models-assisted Algorithm Search.<n>Our findings reveal that LAS landscapes are highly multimodal and rugged.<n>We also demonstrate how population size influences exploration-exploitation trade-offs and the evolving trajectory of elite algorithms.
• Score: 15.767411435705752
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large Language Models (LLMs) have demonstrated significant potential in algorithm design. However, when integrated into search frameworks for iterative algorithm search, the underlying fitness landscape--critical for understanding search behaviou--remains underexplored. In this paper, we illustrate and analyze the fitness landscape of LLM-assisted Algorithm Search (LAS) using a graph-based approach, where nodes represent algorithms and edges denote transitions between them. We conduct extensive evaluations across six algorithm design tasks and six commonly used LLMs. Our findings reveal that LAS landscapes are highly multimodal and rugged, particularly in combinatorial optimization tasks, with distinct structural variations across tasks and LLMs. For instance, heuristic design tasks exhibit dense clusters of high-performing algorithms, while symbolic regression tasks show sparse, scattered distributions. Additionally, we demonstrate how population size influences exploration-exploitation trade-offs and the evolving trajectory of elite algorithms. These insights not only advance our understanding of LAS landscapes but also provide practical guidance for designing more effective LAS methods.

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