Inspiration-Triggered Search : Towards Higher Complexities by Mimicking Creative Processes

Abstract

Open-ended evolution is still an unachieved goal in evolutionary computation. Evolution guided by objective functions can easily be trapped on local optima. Our approach is inspired by evolutionary paths along stepping stones, as observed in user behaviors of Picbreeder. We propose a general framework, inspiration-triggered search, which tries to roughly mimic the creative design process of a human being. Instead of using a fixed objective function, the search algorithm itself is free to switch between objectives within certain constraints but inspired by features of the currently evolved artifacts. The overall optimization task is to generate complex artifacts that cannot be generated by a direct optimization approach. In contrast to other approaches that make extensive use of external knowledge (e.g., Innovation Engines), we try to approach the ambitious goal of virtually bootstrapping a creative process from scratch. The proposed method is tested in the domain of images, that is to find complex and aesthetically pleasant images, and is compared to novelty search.