1. Gabor, T., Illium, S., Zorn, M., and Linnhoff-Popien, C. 2021. Goals for self-replicating neural networks. Artificial Life Conference Proceedings 33, MIT Press One Rogers Street, Cambridge, MA 02142-1209, USA journals-info …, 101.

Building upon the concept of self-replicating neural networks, this research explores the integration of auxiliary functional goals alongside the primary objective of self-replication. The aim is to create networks that can not only reproduce their own weights but also perform useful computations or interact meaningfully with an environment simultaneously.

Analysis graphs or visualizations related to dual-task self-replicating networks
Analysis of networks balancing self-replication and auxiliary tasks.


The study introduces a methodology for dual-task training, utilizing distinct input/output vectors to manage both the replication process and the execution of a secondary task. A key finding is that the presence of an auxiliary task does not necessarily hinder self-replication; instead, it can sometimes complement and even stabilize the replication dynamics.

Further investigations were conducted within the framework of an “artificial chemistry” environment, where populations of these dual-task networks interact:

  • The impact of varying action parameters (related to the secondary task) on the collective learning or emergent behavior of the network population was examined.
  • A concept of a specially designed “guiding particle” network was introduced. This network influences its peers, demonstrating a mechanism for potentially steering the population’s evolution towards desired goal-oriented behaviors.

This work provides insights into how functional complexity can be integrated with self-replication in computational systems, offering potential pathways for developing more sophisticated artificial life models and exploring guided evolution within network populations. [Gabor et al. 2021]