Meta-Learning an Evolvable Developmental Encoding

TL;DR

This paper introduces a meta-learning system that evolves neural cellular automata to create adaptable, high-quality, diverse representations for black-box optimization, inspired by biological development processes.

Contribution

It presents a novel meta-learning approach that evolves developmental encodings, improving evolvability, search speed, and diversity in generated solutions.

Findings

Evolved neural cellular automata can generate diverse 2D maze structures.

Meta-learned representations become more evolvable over time.

The approach enhances search efficiency and solution quality.

Abstract

Representations for black-box optimisation methods (such as evolutionary algorithms) are traditionally constructed using a delicate manual process. This is in contrast to the representation that maps DNAs to phenotypes in biological organisms, which is at the hear of biological complexity and evolvability. Additionally, the core of this process is fundamentally the same across nearly all forms of life, reflecting their shared evolutionary origin. Generative models have shown promise in being learnable representations for black-box optimisation but they are not per se designed to be easily searchable. Here we present a system that can meta-learn such representation by directly optimising for a representation's ability to generate quality-diversity. In more detail, we show our meta-learning approach can find one Neural Cellular Automata, in which cells can attend to different parts of a "DNA" string genome during development, enabling it to grow different solvable 2D maze structures. We show that the evolved genotype-to-phenotype mappings become more and more evolvable, not only resulting in a faster search but also increasing the quality and diversity of grown artefacts.