Generating instances of different properties is key to algorithm selection methods that differentiate between the performance of different solvers for a given combinatorial optimization problem. A wide range of methods using evolutionary computation techniques has been introduced in recent years. With this paper, we contribute to this area of research by providing a new approach based on quality diversity (QD) that is able to explore the whole feature space. QD algorithms allow to create solutions of high quality within a given feature space by splitting it up into boxes and improving solution quality within each box. We use our QD approach for the generation of TSP instances to visualize and analyze the variety of instances differentiating various TSP solvers and compare it to instances generated by a $(μ+1)$-EA for TSP instance generation.
Exploring Definitions of Quality and Diversity in Sonic Measurement Spaces
RITMO, University of Oslo（RITMO，奥斯陆大学）；Department of Informatics, University of Oslo（信息学院，奥斯陆大学）；Department of Musicology, University of Oslo（音乐学系，奥斯陆大学）
Nearest-Better Network for Visualizing and Analyzing Combinatorial Optimization Problems: A Unified Tool
Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies, School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen 518055, China（广东新型安全智能技术重点实验室，计算机科学与技术学院，哈尔滨工业大学，深圳 518055，中国）；School of Artificial Intelligence, Anhui University of Science and Technology, Hefei 231131, China（人工智能学院，安徽理工大学，淮北 231131，中国）；State Key Laboratory of Digital Intelligent Technology for Unmanned Coal Mining, Huainan 232001, China（无人开采数字智能技术国家重点实验室，淮南 232001，中国）；School of Mechanical Engineering and Electronic Information, China University of Geosciences, Wuhan, China（机械工程与电子信息学院，中国地质大学（武汉），中国）；School of Computer Science and Informatics, De Montfort University, Leicester LE1 9BH, U.K.（计算机科学与信息学院，德蒙福特大学，莱斯特 LE1 9BH，英国）；Department of Computer Science, CINVESTAV‑IPN, Mexico City 07360, Mexico（计算机科学系，CINVESTAV‑IPN，墨西哥城 07360，墨西哥）；School of Engineering and Sciences, Tecnológico de Monterrey, Monterrey, N.L., Mexico（工程与科学学院，墨西哥蒙特雷技术学院，墨西哥）
Augmenting Genetic Algorithms with Deep Neural Networks for Exploring the Chemical Space
University of Toronto（多伦多大学）；Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院）；Vector Institute for Artificial Intelligence（矢量人工智能研究所）；Canadian Institute for Advanced Research (CIFAR)（加拿大高等研究院（CIFAR））
Gaussian Smoothen Semantic Features (GSSF) -- Exploring the Linguistic Aspects of Visual Captioning in Indian Languages (Bengali) Using MSCOCO Framework
Computer & Information Science & Engineering Department, University of Florida（佛罗里达大学计算机与信息科学与工程系）
