Swarm Systems as a Platform for Open-Ended Evolutionary Dynamics

TL;DR

Open-ended evolutionary dynamics in artificial systems are explored through heterogeneous swarm frameworks, arguing that Swarm Chemistry provides a powerful platform for indefinite novelty generation. The paper reviews Original, Morphogenetic, and Evolutionary Swarm Chemistry to illustrate how vast design spaces, multiscale emergent patterns, and robust self-organization enable ongoing exploration beyond predefined objectives. It highlights mechanisms such as IEC/HIEC and collision-based information transmission, and discusses implications for science, engineering, and art, as well as practical integration with AI and interpretability challenges. The work outlines future directions for killer applications, theory-to-practice mappings, and cross-disciplinary collaboration with active matter and related fields.

Abstract

Artificial swarm systems have been extensively studied and used in computer science, robotics, engineering and other technological fields, primarily as a platform for implementing robust distributed systems to achieve pre-defined objectives. However, such swarm systems, especially heterogeneous ones, can also be utilized as an ideal platform for creating *open-ended evolutionary dynamics* that do not converge toward pre-defined goals but keep exploring diverse possibilities and generating novel outputs indefinitely. In this article, we review Swarm Chemistry and its variants as concrete sample cases to illustrate beneficial characteristics of heterogeneous swarm systems, including the cardinality leap of design spaces, multiscale structures/behaviors and their diversity, and robust self-organization, self-repair and ecological interactions of emergent patterns, all of which serve as the driving forces for open-ended evolutionary processes. Applications to science, engineering, and art/entertainment as well as the directions of further research are also discussed.

Figures (4)

• Figure 1: Examples of self-organizing dynamic patterns discovered through interactive evolutionary computation in the original Swarm Chemistry. Source: https://bingdev.binghamton.edu/sayama/SwarmChemistry/#recipes. Also see some animated videos on https://www.youtube.com/@ComplexSystem/search?query=swarm%20chemistry.
• Figure 2: Schematic illustrations (left) and snapshots of sample simulation runs (right) of the four classes of Morphogenetic Swarm Chemistry systems defined according to the individual/collective attributes sayama2014four. Each attribute builds upon the previous one. The most basic swarm is a homogeneous one (1st row). Allowing multiple types results in heterogeneous swarms (2nd row), which creates room for individuals to dynamically switch types (3rd row), which then creates room for them to exchange information locally and coordinate their decision making in choosing types (4th row).
• Figure 3: A snapshot of a sample Evolutionary Swarm Chemistry simulation with 10,000 particles. Colors of particles represent the strengths of their three principal behavioral rules ({R, G, B} = {cohesion, alignment, separation}), and thus clusters in different colors represent swarms with different behavioral parameters. A wide variety of different swarm patterns arise and interact throughout the course of simulation. This particular simulation run was initialized using a string "John Horton Conway" as the seed for random numbers to commemorate his life. Watch the full video at https://www.youtube.com/watch?v=YEobkdbCrvQ.
• Figure 4: Collection of sample swarm patterns that were automatically harvested from evolutionary simulation runs sayama2018seeking, showcasing the inherent creativity of the Evolutionary Swarm Chemistry system.