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

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

URL: http://arxiv.org/abs/2602.04529v1
Date: Wed, 04 Feb 2026 13:18:45 GMT
Title: Landscape-aware Automated Algorithm Design: An Efficient Framework for Real-world Optimization
Authors: Haoran Yin, Shuaiqun Pan, Zhao Wei, Jian Cheng Wong, Yew-Soon Ong, Anna V. Kononova, Thomas Bäck, Niki van Stein,
Abstract summary: Large Language Models (LLMs) have opened new frontiers in automated algorithm design.<n>LLMs require extensive evaluation of the target problem to guide the search process.<n>This research proposes an innovative and efficient framework that decouples algorithm discovery from high-cost evaluation.
Score: 32.203665659052845
License: http://creativecommons.org/licenses/by-nc-sa/4.0/
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.

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