Learning Value Functions in Interactive Evolutionary Multiobjective Optimization
Learning Value Functions in Interactive Evolutionary Multiobjective Optimization
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DOI：
10.1109/tevc.2014.2303783
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发表时间：
2015-02-01
影响因子：
14.3
通讯作者：
Zielniewicz, Piotr
中科院分区：
文献类型：
--
作者：
Branke, Juergen;Greco, Salvatore;Zielniewicz, Piotr
本文提出了一种交互式多目标进化算法（MOEA），该算法试图学习捕捉用户真实偏好的价值函数。每隔一段时间，要求用户对单个解决方案进行排序。该信息用于更新算法的内部价值函数模型，并在后续代中使用该模型根据优势度对解进行不可比较排序。这加速了向用户最期望的帕累托锋区域的进化。我们考虑到最一般的附加价值函数作为偏好模型，我们通过经验比较不同的方法来确定相对于给定的偏好信息，不同类型的用户偏好以及在MOEA中使用学习价值函数的不同方法，似乎是最具代表性的价值函数。许多不同场景的结果表明，所提出的算法在一系列基准问题和用户偏好类型上都能很好地工作。
This paper proposes an interactive multiobjective evolutionary algorithm (MOEA) that attempts to learn a value function capturing the users' true preferences. At regular intervals, the user is asked to rank a single pair of solutions. This information is used to update the algorithm's internal value function model, and the model is used in subsequent generations to rank solutions incomparable according to dominance. This speeds up evolution toward the region of the Pareto front that is most desirable to the user. We take into account the most general additive value function as a preference model and we empirically compare different ways to identify the value function that seems to be the most representative with respect to the given preference information, different types of user preferences, and different ways to use the learned value function in the MOEA. Results on a number of different scenarios suggest that the proposed algorithm works well over a range of benchmark problems and types of user preferences.