Miras Karine
Computer Science Department, Vrije Universiteit Amsterdam, Amsterdam, Netherlands.
Front Robot AI. 2021 Jun 15;8:672379. doi: 10.3389/frobt.2021.672379. eCollection 2021.
Genetic encodings and their particular properties are known to have a strong influence on the success of evolutionary systems. However, the literature has widely focused on studying the effects that encodings have on performance, i.e., fitness-oriented studies. Notably, this anchoring of the literature to performance is limiting, considering that performance provides bounded information about the behavior of a robot system. In this paper, we investigate how genetic encodings constrain the space of robot phenotypes and robot behavior. In summary, we demonstrate how two generative encodings of different nature lead to very different robots and discuss these differences. Our principal contributions are creating awareness about robot encoding biases, demonstrating how such biases affect evolved morphological, control, and behavioral traits, and finally scrutinizing the trade-offs among different biases.
众所周知,遗传编码及其特定属性对进化系统的成功有着重大影响。然而,文献广泛聚焦于研究编码对性能的影响,即面向适应性的研究。值得注意的是,鉴于性能仅提供关于机器人系统行为的有限信息,将文献锚定在性能上具有局限性。在本文中,我们研究遗传编码如何限制机器人表型和机器人行为的空间。总之,我们展示了两种不同性质的生成编码如何导致截然不同的机器人,并讨论了这些差异。我们的主要贡献在于提高对机器人编码偏差的认识,展示此类偏差如何影响进化出的形态、控制和行为特征,最后审视不同偏差之间的权衡。