Non-Spatial Hash Chemistry as a Minimalistic Open-Ended Evolutionary System

TL;DR

This paper introduces a simplified non-spatial version of Hash Chemistry, reducing computational costs and enabling more unbounded growth of complex entities, thus advancing open-ended evolutionary systems in artificial life.

Contribution

A novel non-spatial Hash Chemistry model that significantly lowers computational costs and promotes unbounded complexity growth in open-ended evolution.

Findings

Reduced computational costs compared to the original model

More significant unbounded growth of higher-order entities

Demonstrated effectiveness as a minimalistic open-ended system

Abstract

There is an increasing level of interest in open-endedness in the recent literature of Artificial Life and Artificial Intelligence. We previously proposed the cardinality leap of possibility spaces as a promising mechanism to facilitate open-endedness in artificial evolutionary systems, and demonstrated its effectiveness using Hash Chemistry, an artificial chemistry model that used a hash function as a universal fitness evaluator. However, the spatial nature of Hash Chemistry came with extensive computational costs involved in its simulation, and the particle density limit imposed to prevent explosion of computational costs prevented unbounded growth in complexity of higher-order entities. To address these limitations, here we propose a simpler non-spatial variant of Hash Chemistry in which spatial proximity of particles are represented explicitly in the form of multisets. This model modification achieved a significant reduction of computational costs in simulating the model. Results of numerical simulations showed much more significant unbounded growth in both maximal and average sizes of replicating higher-order entities than the original model, demonstrating the effectiveness of this non-spatial model as a minimalistic example of open-ended evolutionary systems.