Evolving inductive generalization via genetic self-assembly

TL;DR

This paper introduces a genetic encoding method for self-assembling components that significantly improves the evolution of complex, scalable systems capable of inductive generalization, demonstrated through simulations of digital circuitry including multiplication.

Contribution

It presents a novel genetic encoding approach for self-assembly that enhances the evolution of complex, generalizable systems, extending capabilities beyond small-scale solutions.

Findings

Successful evolution of scalable digital circuits including multiplication

Self-assembly encoding enables general solutions to problems with infinite instances

Highlights evolutionary and practical relevance of self-assembly in complex system design

Abstract

We propose that genetic encoding of self-assembling components greatly enhances the evolution of complex systems and provides an efficient platform for inductive generalization, i.e. the inductive derivation of a solution to a problem with a potentially infinite number of instances from a limited set of test examples. We exemplify this in simulations by evolving scalable circuitry for several problems. One of them, digital multiplication, has been intensively studied in recent years, where hitherto the evolutionary design of only specific small multipliers was achieved. The fact that this and other problems can be solved in full generality employing self-assembly sheds light on the evolutionary role of self-assembly in biology and is of relevance for the design of complex systems in nano- and bionanotechnology.