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Algorithm Evolution Using Large Language Model
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Optimization can be found in many real-life applications. Designing an effective algorithm for a specific optimization problem typically requires a tedious amount of effort from human experts with domain knowledge and algorithm design skills. In this paper, we propose a novel approach called Algorithm Evolution using Large Language Model (AEL). It utilizes a large language model (LLM) to automatically generate optimization algorithms via an evolutionary framework. AEL does algorithm-level evolution without model training. Human effort and requirements for domain knowledge can be significantly reduced. We take constructive methods for the salesman traveling problem as a test example, we show that the constructive algorithm obtained by AEL outperforms simple hand-crafted and LLM-generated heuristics. Compared with other domain deep learning model-based algorithms, these methods exhibit excellent scalability across different problem sizes. AEL is also very different from previous attempts that utilize LLMs as search operators in algorithms.
Forward citations
Cited by 5 Pith papers
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PathWise: Planning through World Model for Automated Heuristic Design via Self-Evolving LLMs
PathWise generates better optimization heuristics with fewer evaluations by planning over a graph memory of past derivations using multiple LLM agents.
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EALG: Evolutionary Adversarial Generation of Language Model-Guided Generators for Combinatorial Optimization
EALG uses LLMs in an evolutionary adversarial loop to generate increasingly hard TSP instances and heuristics that beat existing LLM-designed solvers on those instances and on TSPLIB.
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LLaMEA-BO: A Large Language Model Evolutionary Algorithm for Automatically Generating Bayesian Optimization Algorithms
An LLM-based evolutionary framework, LLaMEA-BO, automatically writes complete Bayesian optimization algorithms that outperform several state-of-the-art baselines on BBOB and Bayesmark.
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REMoH: A Reflective Evolution of Multi-objective Heuristics approach via Large Language Models
REMoH evolves LLM-written heuristics with NSGA-II and a reflection mechanism, reporting competitive FJSSP results that are weakened by test-set selection.
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A Systematic Survey on Large Language Models for Evolutionary Optimization: From Modeling to Solving
A literature survey that classifies LLM-based optimization research into modeling and solving, with solving divided into LLMs as optimizers, low-level components, and high-level managers.
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