论文标题
美学进化的质量多样性
Quality-diversity for aesthetic evolution
论文作者
论文摘要
许多创造性的生成设计空间都包含具有高审美价值的个人的多个区域。然而，传统的进化计算方法通常集中于优化，寻找人群中最合适的人。在本文中，我们应用质量多样性搜索方法来探索创意生成系统（基于代理的线路绘图模型）。我们对基因型空间进行随机抽样，并使用对美学质量的单个艺术家分配的评估来制定特定于艺术家和该系统的可计算健身措施。为了计算多样性，我们使用卷积神经网络来区分尺寸缩小为二维的特征。我们表明，质量多样性搜索能够找到高审美价值的多种表型。这些表型比艺术家能够使用手动搜索方法找到的表型更高的多样性和质量。
Many creative generative design spaces contain multiple regions with individuals of high aesthetic value. Yet traditional evolutionary computing methods typically focus on optimisation, searching for the fittest individual in a population. In this paper we apply quality-diversity search methods to explore a creative generative system (an agent-based line drawing model). We perform a random sampling of genotype space and use individual artist-assigned evaluations of aesthetic quality to formulate a computable fitness measure specific to the artist and this system. To compute diversity we use a convolutional neural network to discriminate features that are dimensionally reduced into two dimensions. We show that the quality-diversity search is able to find multiple phenotypes of high aesthetic value. These phenotypes show greater diversity and quality than those the artist was able to find using manual search methods.
