Instruction subsets are heuristics that can reduce the size of the inductive programming search space by tens of orders of magnitude. Comprising many overlapping subsets of different sizes, they serve as predictions of the instructions required to code a solution for any problem. Currently, this approach employs a single, large family of subsets meaning that some problems can search thousands of subsets before a solution is found. In this paper we introduce the use of test case type signatures as hyper-heuristics to select one of many, smaller families of instruction subsets. The type signature for any set of test cases maps directly to a single family and smaller families mean that fewer subsets need to be considered for most problems. Having many families also permits subsets to be reordered to better reflect their relative occurrence in human code - again reducing the search space size for many problems. Overall the new approach can further reduce the size of the inductive programming search space by between 1 and 3 orders of magnitude, depending on the type signature. Larger and more consistent reductions are possible through the use of more sophisticated type systems. The potential use of additional test case features as hyper-heuristics and some other possible future work is also briefly discussed.
Key Principles in Cross-Domain Hyper-Heuristic Performance
Faculty of Informatics, Masaryk University（信息技术学院，马萨里克大学）；Christian Doppler Laboratory for Artificial Intelligence and Optimization for Planning and Scheduling, DBAI, TU Wien（人工智能与规划调度优化的克里斯蒂安·多普勒实验室，DBAI，维也纳技术大学）
ReEvo: Large Language Models as Hyper-Heuristics with Reflective Evolution
Peking University（北京大学）；Southeast University（东南大学）；Singapore Management University（新加坡管理大学）；KAIST（韩国科学技术院）；PKU-Wuhan Institute for Artificial Intelligence（北京大学武汉人工智能研究院）
Randomized HyperSteiner: A Stochastic Delaunay Triangulation Heuristic for the Hyperbolic Steiner Minimal Tree
School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology（KTH皇家理工学院电气工程与计算机科学学院）；Amsterdam Machine Learning Lab (AMLab), University of Amsterdam（阿姆斯特丹大学阿姆斯特丹机器学习实验室）；School of Engineering Sciences, KTH Royal Institute of Technology（KTH皇家理工学院工程科学学院）；Leiden Institute of Advanced Computer Science, Universiteit Leiden（莱顿大学莱顿高级计算机科学研究所）
MAP-Elites based Hyper-Heuristic for the Resource Constrained Project Scheduling Problem
Commonwealth Scientific and Industrial Research Organisation（联邦科学与工业研究组织）；Indian Institute of Technology（印度理工学院）；University of New South Wales（新南威尔士大学）
Heuristic Classification of Thoughts Prompting (HCoT): Integrating Expert System Heuristics for Structured Reasoning into Large Language Models
Computer Network Information Center, Chinese Academy of Sciences（中国科学院计算机网络信息中心）；Shenyang Aerospace University（沈阳航空航天大学）；ZGC LAB, Beijing,China（北京ZGC实验室）；Institute for Network Sciences and Cyberspace, Tsinghua University（清华大学网络科学与网络空间研究院）
Selection Hyper-heuristics Can Automatically Adjust the Learning Period to Optimally Solve Pseudo-Boolean Problems
Laboratoire d’Informatique (LIX), CNRS, École Polytechnique, Institut Polytechnique de Paris（信息实验室（LIX），法国国家科学研究中心，巴黎高等理工学院，巴黎理工学院）；Department of Computer Science and Engineering, Southern University of Science and Technology（计算机科学与工程系，南方科技大学）；School of Computing & Communications, Lancaster University Leipzig（计算与通信学院，莱斯特大学莱比锡分校）
Low-Cost Labels, Reliable Choices: Rollout-Calibrated Hyper-Heuristics for Job Shop Scheduling
Faculty of Applied Sciences, Macao Polytechnic University（澳门理工学院应用科学学院）；Pazhou Lab (Huangpu), Guangzhou（广州 Pazhou 实验室（黄埔））；College of Animal Science and Technology, Zhongkai University of Agriculture and Engineering（仲恺农业工程学院动物科学与技术学院）；Macao Polytechnic University（澳门理工学院）
A document is worth a structured record: Principled inductive bias design for document recognition
Centre for AI（人工智能中心）；Zurich University of Applied Sciences（应用科学大学 Zurich）；Institute of Product Development and Production Technologies（产品开发与生产技术研究所）；Institute of Neuroinformatics（神经信息研究所）；University of Zurich and ETH Zurich（苏黎世大学和苏黎世联邦理工学院）；European Centre for Living Technology（欧洲生活科技中心）；RWAI Schweiz AG（RWAI瑞士有限公司）
Accelerated Genetic Programming Hyper-Heuristics for Simulation-Based Scheduling via Agentic AI
Research Engineering Team REANNZ New Zealand；Centre for Data Science；Artificial Intelligence \& School of Engineering；Computer Science Victoria University of Wellington New Zealand
