From Heuristic Selection to Automated Algorithm Design: LLMs Benefit from Strong Priors

• URL: http://arxiv.org/abs/2603.02792v1
• Date: Tue, 03 Mar 2026 09:27:52 GMT
• Title: From Heuristic Selection to Automated Algorithm Design: LLMs Benefit from Strong Priors
• Authors: Qi Huang, Furong Ye, Ananta Shahane, Thomas Bäck, Niki van Stein,
• Abstract summary: Large Language Models (LLMs) have been widely adopted for automated algorithm design.<n>We show that providing high-quality algorithmic code examples can substantially improve the performance of the LLM-driven optimization.
• Score: 4.253872963674906
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Language Models (LLMs) have already been widely adopted for automated algorithm design, demonstrating strong abilities in generating and evolving algorithms across various fields. Existing work has largely focused on examining their effectiveness in solving specific problems, with search strategies primarily guided by adaptive prompt designs. In this paper, through investigating the token-wise attribution of the prompts to LLM-generated algorithmic codes, we show that providing high-quality algorithmic code examples can substantially improve the performance of the LLM-driven optimization. Building upon this insight, we propose leveraging prior benchmark algorithms to guide LLM-driven optimization and demonstrate superior performance on two black-box optimization benchmarks: the pseudo-Boolean optimization suite (pbo) and the black-box optimization suite (bbob). Our findings highlight the value of integrating benchmarking studies to enhance both efficiency and robustness of the LLM-driven black-box optimization methods.

Related papers

• Large Language Model Assisted Automated Algorithm Generation and Evolution via Meta-black-box optimization [9.184788298623062]
  AwesomeDE is proposed that leverages large language models (LLMs) as the strategy of meta-optimizer to generate update rules for constrained evolutionary algorithm without human intervention.<n>Key components, including prompt design and iterative refinement, are systematically analyzed to determine their impact on design quality.<n> Experimental results demonstrate that the proposed approach outperforms existing methods in terms of computational efficiency and solution accuracy.
  arXiv  Detail & Related papers  (2025-09-16T17:02:24Z)
• Combinatorial Optimization for All: Using LLMs to Aid Non-Experts in Improving Optimization Algorithms [0.9668407688201361]
  Large Language Models (LLMs) have shown notable potential in code generation for optimization algorithms.<n>This paper examines how LLMs, rather than creating algorithms from scratch, can improve existing ones without the need for specialized expertise.
  arXiv  Detail & Related papers  (2025-03-14T00:26:00Z)
• Improving Existing Optimization Algorithms with LLMs [0.9668407688201361]
  This paper investigates how Large Language Models (LLMs) can enhance existing optimization algorithms.<n>Using their pre-trained knowledge, we demonstrate their ability to propose innovative variations and implementation strategies.<n>Our results show that an alternative proposed by GPT-4o outperforms the expert-designed of CMSA.
  arXiv  Detail & Related papers  (2025-02-12T10:58:57Z)
• Optima: Optimizing Effectiveness and Efficiency for LLM-Based Multi-Agent System [75.25394449773052]
  Large Language Model (LLM) based multi-agent systems (MAS) show remarkable potential in collaborative problem-solving.<n>Yet they still face critical challenges: low communication efficiency, poor scalability, and a lack of effective parameter-updating optimization methods.<n>We present Optima, a novel framework that addresses these issues by significantly enhancing both communication efficiency and task effectiveness.
  arXiv  Detail & Related papers  (2024-10-10T17:00:06Z)
• Search-Based LLMs for Code Optimization [16.843870288512363]
  Code written by developers usually suffers from efficiency problems and contain various performance bugs. Recent work regards the task as a sequence generation problem, and resorts to deep learning (DL) techniques such as large language models (LLMs) We propose a search-based LLMs framework named SBLLM that enables iterative refinement and discovery of improved optimization methods.
  arXiv  Detail & Related papers  (2024-08-22T06:59:46Z)
• A Problem-Oriented Perspective and Anchor Verification for Code Optimization [43.28045750932116]
  Large language models (LLMs) have shown remarkable capabilities in solving various programming tasks.<n>This paper investigates the capabilities of LLMs in optimizing code for minimal execution time.
  arXiv  Detail & Related papers  (2024-06-17T16:10:10Z)
• Discovering Preference Optimization Algorithms with and for Large Language Models [50.843710797024805]
  offline preference optimization is a key method for enhancing and controlling the quality of Large Language Model (LLM) outputs. We perform objective discovery to automatically discover new state-of-the-art preference optimization algorithms without (expert) human intervention. Experiments demonstrate the state-of-the-art performance of DiscoPOP, a novel algorithm that adaptively blends logistic and exponential losses.
  arXiv  Detail & Related papers  (2024-06-12T16:58:41Z)
• LLM as a Complementary Optimizer to Gradient Descent: A Case Study in Prompt Tuning [69.95292905263393]
  We show that gradient-based and high-level LLMs can effectively collaborate a combined optimization framework.<n>In this paper, we show that these complementary to each other and can effectively collaborate a combined optimization framework.
  arXiv  Detail & Related papers  (2024-05-30T06:24:14Z)
• Localized Zeroth-Order Prompt Optimization [54.964765668688806]
  We propose a novel algorithm, namely localized zeroth-order prompt optimization (ZOPO) ZOPO incorporates a Neural Tangent Kernel-based derived Gaussian process into standard zeroth-order optimization for an efficient search of well-performing local optima in prompt optimization. Remarkably, ZOPO outperforms existing baselines in terms of both the optimization performance and the query efficiency.
  arXiv  Detail & Related papers  (2024-03-05T14:18:15Z)
• Unleashing the Potential of Large Language Models as Prompt Optimizers: Analogical Analysis with Gradient-based Model Optimizers [108.72225067368592]
  We propose a novel perspective to investigate the design of large language models (LLMs)-based prompts.<n>We identify two pivotal factors in model parameter learning: update direction and update method.<n>We develop a capable Gradient-inspired Prompt-based GPO.
  arXiv  Detail & Related papers  (2024-02-27T15:05:32Z)
• An Empirical Evaluation of Zeroth-Order Optimization Methods on AI-driven Molecule Optimization [78.36413169647408]
  We study the effectiveness of various ZO optimization methods for optimizing molecular objectives. We show the advantages of ZO sign-based gradient descent (ZO-signGD) We demonstrate the potential effectiveness of ZO optimization methods on widely used benchmark tasks from the Guacamol suite.
  arXiv  Detail & Related papers  (2022-10-27T01:58:10Z)
• Efficient Non-Parametric Optimizer Search for Diverse Tasks [93.64739408827604]
  We present the first efficient scalable and general framework that can directly search on the tasks of interest. Inspired by the innate tree structure of the underlying math expressions, we re-arrange the spaces into a super-tree. We adopt an adaptation of the Monte Carlo method to tree search, equipped with rejection sampling and equivalent- form detection.
  arXiv  Detail & Related papers  (2022-09-27T17:51:31Z)
• Meta Learning Black-Box Population-Based Optimizers [0.0]
  We propose the use of meta-learning to infer population-based blackbox generalizations. We show that the meta-loss function encourages a learned algorithm to alter its search behavior so that it can easily fit into a new context.
  arXiv  Detail & Related papers  (2021-03-05T08:13:25Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.