One of the main limitations of scalability in body-brain evolution systems is the representation chosen for encoding creatures. This paper defines a class of representations called generative representations, which are identified by their ability to reuse elements of the genotype in the translation to the phenotype. This paper presents an example of a generative representation for the concurrent evolution of the morphology and neural controller of simulated robots, and also introduces GENRE, an evolutionary system for evolving designs using this representation. Applying GENRE to the task of evolving robots for locomotion and comparing it against a non-generative (direct) representation shows that the generative representation system rapidly produces robots with significantly greater fitness. Analyzing these results shows that the generative representation system achieves better performance by capturing useful bias from the design space and by allowing viable large scale mutations in the phenotype. Generative representations thereby enable the encapsulation, coordination, and reuse of assemblies of parts.

Ouadruued . I Robots G r q o n S Hornb) '. Seichi Takamura. Takashi Ydrnamoto. 2nd J!asahrro Fxjita 'conespcndin_e amhor: Mail Step 269-3 X4SA Xmes Research Center lloffett Field. CA 91035-1000. + h r r u r --A chaiienging task tilai must be accomplished for e \ e q legged robot is creating the walking and running behaviors needed for it to more. In this paper %e describe our s5stem for autonomously eiolviing d5namic gaits on hvo of Sonc's quadruped uses the robot's sensors to compute the qualit\ of a gait without assistance from the eyperimenter. First v e show the e.toIution ,.f trnt n 4 t nn tho nRF\LRnmtntrme mhnt WiMh I.. '. y.." ..VI --.. ..-----_I_ r.--.,--.r-* -the fastest gait. the robot moves at over IOdmin., which is more than f o p body-len,&~s/min. While these first gaits are someahat i050G. 2ui r.0:ii&3zzq z!;G&hi 7srs OE 5aG-d '&e rcbct -6 sensitive to the robot and en\-ironment in which the? are evolved. we then show the evolution of robust dvnamic gdts, one of which Index Terns-genetic algorithm, evolutionary algorithm. dy-Fig, enrer;ainment rohotq. proror).pr: b) ERS-I , .

1https://ntrs.nasa.gov/search.jsp?R=20030107313 2018-05-13T01:23:13+00:00Zwe have built from this process are still very simple compared to human-engineered machines, their structure is more principled (regular, modular and hierarchical) compared to previously evolved machines of comparable functionality, and the virtual designs which are achieved by the system have an order of magnitude more moving parts. Moreover, we quantitatively demonstrate for this .design space how the generative representation is capable of searching more efficiently than a nongenerative representation. Structure of this paperWe will begin with a brief background of evolutionary robotics and related work, and demonstrate the scaling problem with our own prior results. Next we propose the use of an evolved generative representation as opposed to a non-generative representation. We describe this representation in detail as well as the evolutionary process that uses it. We then compare progress of evolved robots with and without the use of the grammar, and quantify the obtained advantage. Working twodimensional and three-dimensional physical robots produced by the system are shown. BackgroundBiological evolution is characterized as a process applied to a population of individuals, which are subject to selective replication with variation (Maynard-Smith and Szathmary, 1995). Evolutionary design systems use the same principles of biological evolution to achieve machine design, yet add a target to the evolution -the functional requirements specified by the designer. Candidate designs in a population are thus still selected, replicated and varied, but selection is governed by an external design criteria. After a number of generations the selective evolutionary process may breed an acceptable design.Genetic algorithms -a subset of evolutionary computation involving mutation and crossover in a population of fixed length bit strings (Holland, 1975) -have been applied for several decades in many engineering problems as an optimization technique for a fixed set of parameters.Alternatively, more recent open-ended evolutionary design systems, in which the process is allowed to add more and more building blocks and parameters, seem particularly adequate to design problems requiring synthesis. Such open-ended evolutionary design systems have been demonstrated for a variety of simple design problems, including structures, mechanisms, software, optics, robotics, control, and many others (for overviews see. for example, Koza, 1992; Bentley, 4 1999; Husbands et nl, 1998). Yet these accomplishments remain simple compared to what teams of human engineers can design and what nature has produced. The evolutionary design approach is often criticized as scaling badly when challenged with design requirements of higher complexity. (Mataric and Cliff, 1996) While there are still many poorly understood factors that determine the success of evolutionary design -such as starting conditions, variation operators, primitive building blocks and fidelity of simulatio...