In this work, we leverage ensemble learning as a tool for the creation of faster, smaller, and more accurate deep learning models. We demonstrate that we can jointly optimize for accuracy, inference time, and the number of parameters by combining DNN classifiers. To achieve this, we combine multiple ensemble strategies: bagging, boosting, and an ordered chain of classifiers. To reduce the number of DNN ensemble evaluations during the search, we propose EARN, an evolutionary approach that optimizes the ensemble according to three objectives regarding the constraints specified by the user. We run EARN on 10 image classification datasets with an initial pool of 32 state-of-the-art DCNN on both CPU and GPU platforms, and we generate models with speedups up to $7.60\times$, reductions of parameters by $10\times$, or increases in accuracy up to $6.01\%$ regarding the best DNN in the pool. In addition, our method generates models that are $5.6\times$ faster than the state-of-the-art methods for automatic model generation.
University of New South Wales（新南威尔士大学）；UNSW Data Science Hub（新南威尔士大学数据科学中心）；Australian Research Council - Industrial Transformation Training Centre（澳大利亚研究委员会工业转型培训中心）
An Evolutionary Algorithm Assisted by an Ensemble of Pareto-Optimal Surrogate Models
Organization for the Promotion of Education, Yokohama National University（横滨国立大学教育推进机构）；School of Engineering, Westlake University（西湖大学工学院）；Faculty of Engineering, Yokohama National University（横滨国立大学工学部）
Structure-Aware Cooperative Ensemble Evolutionary Optimization on Combinatorial Problems with Multimodal Large Language Models
School of Physical and Mathematical Sciences, Nanyang Technological University（南洋理工大学物理与数学科学学院）；College of Computing and Data Science, Nanyang Technological University（南洋理工大学计算与数据科学学院）
A Multi-disciplinary Ensemble Algorithm for Clustering Heterogeneous Datasets
Kurdistan Institution for Strategic Studies and Scientific Research（库尔德斯坦战略研究与科学研究机构）；University of Kurdistan Hewler（库尔德斯坦赫勒大学）；Sulaimani Polytechnic University（苏莱曼尼理工大学）
Mitigating Unfairness via Evolutionary Multi-objective Ensemble Learning
Research Institute of Trustworthy Autonomous System, Southern University of Science and Technology（南方科技大学可信自主系统研究院）；Trustworthiness Theory Research Center, Huawei Technologies Co., Ltd.（华为技术有限公司可信理论研究中心）；School of Computer Science, University of Birmingham（伯明翰大学计算机学院）
Multi-population Ensemble Genetic Programming via Cooperative Coevolution and Multi-view Learning for Classification
Faculty of Engineering & Information Technology, University of Technology Sydney（工程与信息科技学院，技术悉尼大学）；Department of Civil and Architectural Engineering, Sultan Qaboos University（土木与建筑工程系，苏丹·卡布斯大学）；University Research and Innovation Center (EKIK), Obuda University（研究与创新中心（EKIK），奥布达大学）
