论文标题
山谷聚类的实用值进化的多模式多模式多模式优化
Real-valued Evolutionary Multi-modal Multi-objective Optimization by Hill-Valley Clustering
论文作者
论文摘要
在基于模型的进化算法（EAS）中，基础搜索分布适用于手头的问题，例如基于决策变量之间的依赖关系。 Hill-Valley聚类是一种自适应方法，其中将一组解决方案聚类，使每个群集都对应于健身景观中的单个模式。这可以用来使EA的搜索分布适用于模式的数量，并分别探索每个模式。尤其是在黑框设置中，模式数量是先验的未知，一种自适应方法对于良好的性能至关重要。在这项工作中，我们引入了多目标山谷聚类，并将其与多目标EA Mamalgam结合到多目标Hill-Valley EA（Mo-Hillvallea）中。我们从经验上表明，Mo-Hillvallea在一组基准函数上胜过Mamalgam和其他众所周知的多目标优化算法。此外，也许最重要的是，我们表明Mo-Hillvallea能够随着时间的推移同时获得和维持多个近似集。
In model-based evolutionary algorithms (EAs), the underlying search distribution is adapted to the problem at hand, for example based on dependencies between decision variables. Hill-valley clustering is an adaptive niching method in which a set of solutions is clustered such that each cluster corresponds to a single mode in the fitness landscape. This can be used to adapt the search distribution of an EA to the number of modes, exploring each mode separately. Especially in a black-box setting, where the number of modes is a priori unknown, an adaptive approach is essential for good performance. In this work, we introduce multi-objective hill-valley clustering and combine it with MAMaLGaM, a multi-objective EA, into the multi-objective hill-valley EA (MO-HillVallEA). We empirically show that MO-HillVallEA outperforms MAMaLGaM and other well-known multi-objective optimization algorithms on a set of benchmark functions. Furthermore, and perhaps most important, we show that MO-HillVallEA is capable of obtaining and maintaining multiple approximation sets simultaneously over time.
