Landscape-aware Automated Algorithm Design: An Efficient Framework for Real-world Optimization

2026-02-04

AI Summary

This work proposes an efficient framework for automatic algorithm design that decouples algorithm discovery from costly real-world evaluations, addressing the limitations of existing large language model–based approaches which require extensive and expensive assessments on actual problems. By leveraging surrogate functions to enable deep exploration in the algorithm space and incorporating problem landscape features to guide the co-evolution of genetic programming and large language models, the method generates optimization algorithms with strong generalization capabilities. Experimental results demonstrate that the proposed approach significantly reduces the number of required real evaluations while successfully discovering high-performance algorithms across multiple practical optimization tasks, thereby validating its effectiveness and practicality in resource-constrained settings.

Technology Category

• Search and Optimization: Evolutionary Computation
• Machine Learning: Optimization
• Natural Language Processing: Code Generation / Program Synthesis from Natural Language

Application Category

• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

The advent of Large Language Models (LLMs) has opened new frontiers in automated algorithm design, giving rise to numerous powerful methods. However, these approaches retain critical limitations: they require extensive evaluation of the target problem to guide the search process, making them impractical for real-world optimization tasks, where each evaluation consumes substantial computational resources. This research proposes an innovative and efficient framework that decouples algorithm discovery from high-cost evaluation. Our core innovation lies in combining a Genetic Programming (GP) function generator with an LLM-driven evolutionary algorithm designer. The evolutionary direction of the GP-based function generator is guided by the similarity between the landscape characteristics of generated proxy functions and those of real-world problems, ensuring that algorithms discovered via proxy functions exhibit comparable performance on real-world problems. Our method enables deep exploration of the algorithmic space before final validation while avoiding costly real-world evaluations. We validated the framework's efficacy across multiple real-world problems, demonstrating its ability to discover high-performance algorithms while substantially reducing expensive evaluations. This approach shows a path to apply LLM-based automated algorithm design to computationally intensive real-world optimization challenges.

Problem

• automated algorithm design
• real-world optimization
• expensive evaluations
• landscape-aware
• Large Language Models

Innovation

• Landscape-aware
• Automated Algorithm Design
• Genetic Programming
• Large Language Models
• Proxy Functions