Dynamic Detection of Relevant Objectives and Adaptation to Preference Drifts in Interactive Evolutionary Multi-Objective Optimization
Centre for Logistics & Sustainability Analytics, Kent Business School, University of Kent（肯特大学肯特商学院物流与可持续性分析中心）；Université de Haute-Alsace（上阿尔萨斯大学）；Alliance Manchester Business School, University of Manchester（曼彻斯特大学联盟曼彻斯特商学院）
Seyed Mahdi Shavarani, Mahmoud Golabi, Richard Allmendinger, Lhassane Idoumghar
Evolutionary Multi-Objective Optimization Algorithms (EMOAs) are widely employed to tackle problems with multiple conflicting objectives. Recent research indicates that not all objectives are equally important to the decision-maker (DM). In the context of interactive EMOAs, preference information elicited from the DM during the optimization process can be leveraged to identify and discard irrelevant objectives, a crucial step when objective evaluations are computationally expensive. However, much of the existing literature fails to account for the dynamic nature of DM preferences, which can evolve throughout the decision-making process and affect the relevance of objectives. This study addresses this limitation by simulating dynamic shifts in DM preferences within a ranking-based interactive algorithm. Additionally, we propose methods to discard outdated or conflicting preferences when such shifts occur. Building on prior research, we also introduce a mechanism to safeguard relevant objectives that may become trapped in local or global optima due to the diminished correlation with the DM-provided rankings. Our experimental results demonstrate that the proposed methods effectively manage evolving preferences and significantly enhance the quality and desirability of the solutions produced by the algorithm.
VINE: An Open Source Interactive Data Visualization Tool for Neuroevolution
Uber AI Labs（优步人工智能实验室）
深度神经进化的最新进展表明，进化算法（如演化策略（ES）和遗传算法（GA））可以扩展到训练深度神经网络，以解决困难的强化学习（RL）问题。然而，在如此高的维度上分析和解释神经进化的潜在过程仍然是一个挑战。为了应对这一挑战，本文提出了一种名为VINE（Visual Inspector for NeuroEvolution）的交互式数据可视化工具，旨在帮助神经进化研究人员和最终用户更好地理解和探索这一系列算法。VINE与广泛的神经进化算法（包括ES和GA）无缝配合，并通过对几代进化代理的行为特征进行交互式可视化来解决观察学习过程底层动态的困难。随着神经进化扩展到具有数百万或更多连接的神经网络，像VINE这样的可视化工具提供了对进化潜在动力学的新见解，对于激发新的创新和应用变得越来越有价值和重要。
