Optimization Algorithm Icon Large Language Model

The ability of Large Language Models LLMs to generate high-quality text and code has fuelled their rise in popularity. In this paper, we aim to demonstrate the potential of LLMs within the realm of optimization algorithms by integrating them into STNWeb. This is a web-based tool for the generation of Search Trajectory Networks STNs, which are visualizations of optimization algorithm

Optimization algorithms are the unsung heroes behind resource allocation, logistics, and even your daily route to work. The traditional methods have been good, but with the rising complexity of problems, 'good' just doesn't cut it anymore. Enter LLMs, which bring a wealth of pre-trained knowledge and adaptability to the table.

Optimization algorithms and large language models LLMs enhance decision-making in dynamic environments by integrating artificial intelligence with traditional techniques. LLMs, with extensive domain knowledge, facilitate intelligent modeling and strategic decision-making in optimization, while optimization algorithms refine LLM architectures and output quality. This synergy offers novel

Introduction LLaMEA Large Language Model Evolutionary Algorithm is an innovative framework that leverages the power of large language models LLMs such as GPT-4 for the automated generation and refinement of metaheuristic optimization algorithms.

Instead of relying on traditional optimization algorithms like RMSPROP, ADAM, and others, the focus shifts to refining the questions or prompts given to the model.

The optimization of algorithms in exact combinatorial optimization CO solver plays a fundamental role in operations research. However, due to the extensive requirements on domain knowledge and the large search space for algorithm design, the refinement on these algorithms remains highly challenging for both manual and learning-based paradigms.

In a recent paper, Camilo Chacon Sartori and Christian Blum explore the intersection of Large Language Models LLMs and optimization algorithms, demonstrating that these models can not only understand but also improve upon complex, expert-designed code. Their work focuses on the Construct, Merge, Solve amp Adapt CMSA algorithm, a hybrid metaheuristic for combinatorial optimization problems

Large language models LLMs, such as GPT-4 have demonstrated their ability to understand natural language and generate complex code snippets. This article introduces a novel LLM evolutionary algorithm LLaMEA framework, leveraging GPT models for the automated generation and refinement of algorithms. Given a set of criteria and a task definition the search space, LLaMEA iteratively

The optimization algorithm typically needs to be customized for an individual task to deal with the specific challenges posed by the decision space and the performance landscape, especially for derivative-free optimization.

Connecting Large Language Models with Evolutionary Algorithms Yields Powerful Prompt Optimizers Qingyan Guo Rui Wang Junliang Guo Bei Li Kaitao Song Xu Tan Guoqing Liu Jiang Bian Yujiu Yang