Boolean Networks Design by Genetic Algorithms

TL;DR

This paper explores using genetic algorithms to design Boolean networks with specific attractor lengths, demonstrating that initial network conditions influence success rates and that critical networks perform best.

Contribution

It introduces a novel approach for automatic Boolean network design using genetic algorithms, analyzing the impact of initial conditions on success.

Findings

Critical and chaotic networks have higher success ratios.

Evolution from critical networks yields the best performance.

All initial network types can reach the target attractor length.

Abstract

We present and discuss the results of an experimental analysis in the design of Boolean networks by means of genetic algorithms. A population of networks is evolved with the aim of finding a network such that the attractor it reaches is of required length $l$. In general, any target can be defined, provided that it is possible to model the task as an optimisation problem over the space of networks. We experiment with different initial conditions for the networks, namely in ordered, chaotic and critical regions, and also with different target length values. Results show that all kinds of initial networks can attain the desired goal, but with different success ratios: initial populations composed of critical or chaotic networks are more likely to reach the target. Moreover, the evolution starting from critical networks achieves the best overall performance. This study is the first step toward the use of search algorithms as tools for automatically design Boolean networks with required properties.