Improving Effectiveness and Efficiency in Wagner’s Modularity-Evolving Artificial Gene Regulatory Networks

Although no consensus has been reached on the conditions under which modularity emerges, we lind the idea of specialisation driving modularity highly plausible. Current abstractions have demonstrated emergence of modularity in scenarios evolving Gene Regulatory Networks to show simple targeted behaviours, but these methods are less successful in more complex, real-world-level scenarios. We aim to study methods that are more efficient and effective in these simple scenarios, aiming to extend them to higher complexity. We engineer two modifications to the system that advance this aim. Progressive selection allows more than an order of magnitude reduction in computational cost without damaging the emergence of modularity, while dynamic specialisation produces significant improvements in modularity while also modestly reducing computational costs. We also provide some insight into the first of the two evolutionary stages in this approach, providing some explanation of why dynamic specialisation works.