Quality-Diversity (QD) algorithms are a recent type of optimisation methods that search for a collection of both diverse and high performing solutions. They can be used to effectively explore a target problem according to features defined by the user. However, the field of QD still does not possess extensive methodologies and reference benchmarks to compare these algorithms. We propose a simple benchmark to compare the reliability of QD algorithms by optimising the Rastrigin function, an artificial landscape function often used to test global optimisation methods.
Evolutionary foraging in grids: Intermittent search dynamics emerge in finite, depletable landscapes
OsloMet – Oslo Metropolitan University（奥斯陆城市大学）；Simula Research Laboratory（西穆拉研究实验室）；University of Oslo（奥斯陆大学）；Kristiania University of Applied Sciences（克里斯蒂尼亚应用科学大学）
Vector Quantized-Elites: Unsupervised and Problem-Agnostic Quality-Diversity Optimization
Computational Intelligence Laboratory (CILab), Department of Mathematics, University of Patras（数学系计算智能实验室（CILab），帕特拉大学）；Laboratory of Automation and Robotics (LAR) in the Department of Electrical & Computer Engineering, University of Patras（电气与计算机工程系自动化与机器人实验室（LAR），帕特拉大学）
LLM Driven Design of Continuous Optimization Problems with Controllable High-level Properties
Machine Learning and Optimization, Paderborn University, Germany（机器学习与优化，帕德博恩大学，德国）；Leiden Institute of Advanced Computer Science, Leiden University, The Netherlands（莱顿先进计算机科学研究所，莱顿大学，荷兰）；Data Management and Biometrics, University of Twente, The Netherlands（数据管理与生物特征，埃因霍温大学，荷兰）
Quality-Diversity Optimization: a novel branch of stochastic optimization
Computer Technology Institute & Press “Diophantus” (CTI)（“丢番图”计算机技术研究所与出版社（CTI））；Imperial College London（伦敦帝国学院）；Research Centre on Interactive Media, Smart Systems and Emerging Technologies（互动媒体、智能系统与新兴技术研究中心）；Inria（法国国家信息与自动化研究所（Inria））；CNRS（法国国家科学研究中心（CNRS））；Université de Lorraine（洛林大学）
