Generative Representations for Design Automation

Recent research has demonstrated the ability for the automatic design of basic shapes and the morphology and control of real physical robots using techniques inspired by biological evolution. The main criticism of the evolutionary design approach, however, is that it is doubtful whether it will reach the high complexities necessary for practical engineering.

Here we claim that for automatic design systems to scale in complexity the designs they produce must be made of re-used modules. Our approach is based on the use of a generative representation as the method to encode individuals in the population. Unlike a direct representation of a design, a generative representation is an algorithm for creating a design. That is, the data being optimized by the search algorithm is itself a kind of program containing rules and program-like instructions for generating a design, with the ability to re-use parts of the program in a modular way allowing it to create more complex parts from simpler parts and re-use a component multiple times in a design.

We describe a system for creating generative specifications capable of hierarchical regularity by using Lindenmayer systems (L-systems) as the generative representation for an evolutionary algorithms. Using this system we demonstrate a system that evolves hierarchically modular tables and locomoting robots (called genobots).

An example L-system.

The sequence of assembly procedures it generates.