On the Entanglement between Evolvability and Fitness: an Experimental Study on Voxel-based Soft Robots

Andrea Ferigo, L. Soros, E. Medvet, Giovanni Iacca
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引用次数: 5

Abstract

The concept of evolvability, that is the capacity to produce heritable and adaptive phenotypic variation, is crucial in the current understanding of evolution. However, while its meaning is intuitive, there is no consensus on how to quantitatively measure it. As a consequence, in evolutionary robotics, it is hard to evaluate the interplay between evolvability and fitness and its dependency on key factors like the evolutionary algo-rithm (EA) or the representation of the individuals. Here, we propose to use MAP-Elites, a well-established Quality Diversity EA, as a support structure for measuring evolvability and for highlighting its interplay with fitness. We map the solutions generated during the evolutionary process to a MAP-Elites-like grid and then visualize their fitness and evolvability as maps. This procedures does not affect the EA execution and can hence be applied to any EA: it only requires to have two descriptors for the solutions that can be used to meaning-fully characterize them. We apply this general methodology to the case of Voxel-based Soft Robots (VSR), a kind of modular robots with a body composed of uniform elements whose volume is individually varied by the robot brain. Namely, we optimize the robots for the task of locomotion using evolutionary computation. We consider four representations, i.e., ways of transforming a genotype into a robot, two for the brain only and two for both body and brain of the VSR, and two EAs (MAP-Elites and a simple evolutionary strategy) and examine the evolvability and fitness maps. The experiments suggest that our methodology permits us to discover interesting patterns in the maps: fitness maps appear to depend more on the representation of the solution, whereas evolv-ability maps appear to depend more on the EA. As an aside, we find that MAP-Elites is particularly effective in the simultaneous evolution of the body and the brain of Voxel-based Soft Robots.
基于体素的软体机器人可进化性与适应度的纠缠研究
可进化性的概念,即产生可遗传和适应性表型变异的能力,在当前对进化的理解中是至关重要的。然而,虽然它的含义是直观的,但对于如何定量衡量它却没有共识。因此,在进化机器人中,很难评估可进化性和适应度之间的相互作用,以及它对进化算法(EA)或个体表示等关键因素的依赖。在这里,我们建议使用MAP-Elites(一个完善的质量多样性EA)作为测量可进化性和突出其与适应度的相互作用的支持结构。我们将进化过程中产生的解决方案映射到一个类似map - elite的网格中,然后将它们的适应度和进化性可视化为地图。这个过程不会影响EA的执行,因此可以应用到任何EA:它只需要有两个解决方案的描述符,可以用来有意义地描述它们。我们将这种通用方法应用于基于体素的软机器人(VSR),这是一种模块化机器人,其身体由统一的元素组成,其体积由机器人大脑单独改变。也就是说,我们使用进化计算优化机器人的运动任务。我们考虑了四种表达方式,即将基因型转化为机器人的方式,两种仅用于大脑,两种用于VSR的身体和大脑,以及两种ea (map - elite和简单进化策略),并检查了可进化性和适应度图。实验表明,我们的方法允许我们在地图中发现有趣的模式:适应性地图似乎更多地依赖于解决方案的表示,而进化能力地图似乎更多地依赖于EA。顺便说一下,我们发现map - elite在基于体素的软机器人的身体和大脑的同时进化中特别有效。
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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