Evolving aesthetic images using multiobjective optimization

G. Greenfield
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引用次数: 30

Abstract

We consider the problem of using evolutionary multiobjective optimization to evolve visual imagery. In our method, images (phenomes) are generated from expressions (genomes), and then color segmented so that they can be evaluated under a number of different aesthetic criteria. Our principal task is to formulate fitness functions that make the best use of these elementary aesthetic components. We demonstrate the benefits obtained from using more than one objective function. We also discuss technical issues that arose as a consequence of treating our computational aesthetics problem as a "real-world" application of evolutionary multiobjective optimization.
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基于多目标优化的美学图像演化
我们考虑了使用进化多目标优化来进化视觉图像的问题。在我们的方法中,图像(现象)是从表达式(基因组)生成的,然后进行颜色分割,以便可以在许多不同的美学标准下对它们进行评估。我们的主要任务是制定适应度函数,以充分利用这些基本的美学成分。我们演示了使用多个目标函数所获得的好处。我们还讨论了由于将计算美学问题视为进化多目标优化的“现实世界”应用而产生的技术问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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