Neural architecture search (NAS), the study of automating the discovery of optimal deep neural network architectures for tasks in domains such as computer vision and natural language processing, has seen rapid growth in the machine learning research community. While there have been many recent advancements in NAS, there is still a significant focus on reducing the computational cost incurred when validating discovered architectures by making search more efficient. Evolutionary algorithms, specifically genetic algorithms, have a history of usage in NAS and continue to gain popularity versus other optimization approaches as a highly efficient way to explore the architecture objective space. Most NAS research efforts have centered around computer vision tasks and only recently have other modalities, such as the rapidly growing field of natural language processing, been investigated in depth. In this work, we show how genetic algorithms can be paired with lightly trained objective predictors in an iterative cycle to accelerate multi-objective architectural exploration in a way that works in the modalities of both machine translation and image classification.
Accelerating Evolution: Integrating PSO Principles into Real-Coded Genetic Algorithm Crossover
Xiaobo Jin School of Information Engineering Taizhou Vocational College of Science & Technology（金晓波 学校信息工程学院 太zhou 职业科技学院）；Traditional Chinese Medicine department Taizhou First People’s Hospital（传统中医部门 太zhou 第一人民医院）
Accelerating the Genetic Algorithm for Large-scale Traveling Salesman Problems by Cooperative Coevolutionary Pointer Network with Reinforcement Learning
Grad. School of Info. Science；Technology Hokkaido University Sapporo, Japan \ .；Technology Hokkaido University Sapporo, Japan；Information Initiative Center Hokkaido University Sapporo, Japan
Evolving Efficient Genetic Encoding for Deep Spiking Neural Networks
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；Center for Long-term Artificial Intelligence（长期人工智能中心）；Key Laboratory of Brain Cognition and Brain-inspired Intelligence Technology, Chinese Academy of Sciences（中国科学院脑认知与脑智能技术重点实验室）
Synergizing Reinforcement Learning and Genetic Algorithms for Neural Combinatorial Optimization
C 2 DL, Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；School of Advanced Interdisciplinary Sciences, University of Chinese Academy of Sciences（中国科学院大学先进交叉学科学院）；Department of Computer Science and Technology, Tsinghua University（清华大学计算机科学与技术系）
A Modality-Aware Cooperative Co-Evolutionary Framework for Multimodal Graph Neural Architecture Search
Department of Computer Science and Information Technology, La Trobe University（计算机科学与信息技术系，拉特罗布大学）；Institute for Sustainable Industries and Liveable Cities, Victoria University（可持续产业与宜居城市研究所，维多利亚大学）
ERNAS: An Evolutionary Neural Architecture Search for Magnetic Resonance Image Reconstructions
Gordon Center for Medical Imaging, Massachusetts General Hospital and Harvard Medical School（马萨诸塞总医院与哈佛医学院戈登医学影像中心）；Texas A&M University（德克萨斯A&M大学）；Texas A&M Institute of Data Science, Texas A&M University（德克萨斯A&M大学数据科学研究所）；Texas A&M University at Qatar（德克萨斯A&M大学卡塔尔分校）
