报告题目：Evolution of Heuristics: Towards Efficient Automated Algorithm Design Using Large Language Models
报告时间：2024年12月12号下午3点
报告地点：信息楼330
报告提要：Heuristics are widely used for dealing with complex search and optimization problems. However, manual design of heuristics can be often very labour extensive and requires rich working experience and knowledge. This talk will introduce Evolution of Heuristic (EoH), an evolutionary paradigm that leverages both Large Language Models (LLMs) and evolutionary search for Automatic Heuristic Design (AHD). EoH represents the ideas of heuristics in natural language, termed thoughts. They are then translated into executable codes by LLMs. The evolution of both thoughts and codes in an evolutionary search framework makes it very effective and efficient for generating high-performance heuristics. Experiments on three widely studied combinatorial optimization benchmark problems demonstrate that EoH outperforms commonly used handcrafted heuristics and other recent AHD methods including FunSearch proposed by googleDeepMind.
专家简介：Qingfu Zhang is a Chair Professor of Computational Intelligence with the Department of Computer Science, City University of Hong Kong. His is an IEEE fellow. His main research interests include evolutionary computation, optimization, metaheuristic, machine learning and their applications. He leads the Optimization and learning Group in CityU. His MOEA/D algorithms have been widely researched in the EMO field and used in industry. He has been listed as a highly cited researcher in computer science for 8 times.
