The Selfish Algorithm

TL;DR

This paper explores the principle of Generalized Natural Selection (GNS) by proposing a test within Cellular Automata to observe how more computationally sophisticated processes tend to persist over less sophisticated ones given sufficient resources.

Contribution

It introduces a concrete experimental setup to test GNS in Cellular Automata, bridging theoretical principles with practical simulation.

Findings

GNS can be observed in Cellular Automata under certain conditions

More complex computational processes tend to dominate simpler ones when resources are ample

Provides a framework for empirical testing of GNS in computational systems

Abstract

The principle of Generalized Natural Selection (GNS) states that in nature, computational processes of high computational sophistication are more likely to maintain/abide than processes of lower computational sophistication provided that sufficiently many resources are around to sustain the processes. In this paper we give a concrete set-up how to test GNS in a weak sense. In particular, we work in the setting of Cellular Automata and see how GNS can manifest itself in this setting.