Evolution of Optimization Algorithms for Global Placement via Large Language Models

• URL: http://arxiv.org/abs/2504.17801v1
• Date: Fri, 18 Apr 2025 09:57:14 GMT
• Title: Evolution of Optimization Algorithms for Global Placement via Large Language Models
• Authors: Xufeng Yao, Jiaxi Jiang, Yuxuan Zhao, Peiyu Liao, Yibo Lin, Bei Yu,
• Abstract summary: This paper presents an automated framework to evolve optimization algorithms for global placement.<n>We first generate diverse candidate algorithms using large language models (LLM) through carefully crafted prompts.<n>The discovered optimization algorithms exhibit substantial performance improvements across many benchmarks.
• Score: 18.373855320220887
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Optimization algorithms are widely employed to tackle complex problems, but designing them manually is often labor-intensive and requires significant expertise. Global placement is a fundamental step in electronic design automation (EDA). While analytical approaches represent the state-of-the-art (SOTA) in global placement, their core optimization algorithms remain heavily dependent on heuristics and customized components, such as initialization strategies, preconditioning methods, and line search techniques. This paper presents an automated framework that leverages large language models (LLM) to evolve optimization algorithms for global placement. We first generate diverse candidate algorithms using LLM through carefully crafted prompts. Then we introduce an LLM-based genetic flow to evolve selected candidate algorithms. The discovered optimization algorithms exhibit substantial performance improvements across many benchmarks. Specifically, Our design-case-specific discovered algorithms achieve average HPWL improvements of \textbf{5.05\%}, \text{5.29\%} and \textbf{8.30\%} on MMS, ISPD2005 and ISPD2019 benchmarks, and up to \textbf{17\%} improvements on individual cases. Additionally, the discovered algorithms demonstrate good generalization ability and are complementary to existing parameter-tuning methods.

Related papers

• 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)
• From Understanding to Excelling: Template-Free Algorithm Design through Structural-Functional Co-Evolution [39.42526347710991]
  Large language models (LLMs) have greatly accelerated the automation of algorithm generation and optimization.<n>We introduce an end-to-end algorithm generation and optimization framework based on LLMs.<n>Our approach utilizes the deep semantic understanding of LLMs to convert natural language requirements or human-authored papers into code solutions.
  arXiv  Detail & Related papers  (2025-03-13T08:26:18Z)
• Provably Faster Algorithms for Bilevel Optimization via Without-Replacement Sampling [96.47086913559289]
  gradient-based algorithms are widely used in bilevel optimization. We introduce a without-replacement sampling based algorithm which achieves a faster convergence rate. We validate our algorithms over both synthetic and real-world applications.
  arXiv  Detail & Related papers  (2024-11-07T17:05:31Z)
• Can Large Language Models Invent Algorithms to Improve Themselves?: Algorithm Discovery for Recursive Self-Improvement through Reinforcement Learning [3.6117068575553595]
  Self-Developing is a framework that enables Large Language Models to autonomously discover, implement, and refine their own improvement algorithms.<n>We demonstrate this framework through model merging, a practical technique for combining specialized models.<n>On mathematical reasoning benchmarks, the autonomously discovered algorithms improve the seed model's GSM8k performance by 6% and exceed human-designed approaches like Task Arithmetic by 4.3%.
  arXiv  Detail & Related papers  (2024-10-21T04:57:09Z)
• 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)
• LLaMEA: A Large Language Model Evolutionary Algorithm for Automatically Generating Metaheuristics [0.023020018305241332]
  This paper introduces a novel Large Language Model Evolutionary Algorithm (LLaMEA) framework.<n>Given a set of criteria and a task definition (the search space), LLaMEA iteratively generates, mutates and selects algorithms.<n>We show how this framework can be used to generate novel black-box metaheuristic optimization algorithms automatically.
  arXiv  Detail & Related papers  (2024-05-30T15:10:59Z)
• 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)
• Algorithm Evolution Using Large Language Model [18.03090066194074]
  We propose a novel approach called Evolution Algorithm using Large Language Model (AEL) AEL does algorithm-level evolution without model training. Human effort and requirements for domain knowledge can be significantly reduced.
  arXiv  Detail & Related papers  (2023-11-26T09:38:44Z)
• 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)
• Extreme Algorithm Selection With Dyadic Feature Representation [78.13985819417974]
  We propose the setting of extreme algorithm selection (XAS) where we consider fixed sets of thousands of candidate algorithms. We assess the applicability of state-of-the-art AS techniques to the XAS setting and propose approaches leveraging a dyadic feature representation.
  arXiv  Detail & Related papers  (2020-01-29T09:40:58Z)

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.