Evolution is Driven by Natural Autoencoding: Reframing Species, Interaction Codes, Cooperation, and Sexual Reproduction

TL;DR

This paper introduces the concept of natural autoencoding as a fundamental process driving evolution, species formation, and interaction codes, offering a new computational perspective on biological continuity and diversity.

Contribution

It proposes natural autoencoding as a core mechanism of evolution, redefining species and genetic processes beyond traditional genetic paradigms.

Findings

Species are defined by interaction codes stored in biological infrastructure.

Evolution involves autoencoding of changes in interaction codes.

Genome randomization in sexual reproduction aligns with autoencoding principles.

Abstract

The continuity of life and its evolution, we proposed, emerge from an interactive group process manifested in networks of interaction. We term this process \textit{survival-of-the-fitted}. Here, we reason that survival of the fitted results from a natural computational process we term \textit{natural autoencoding}. Natural autoencoding works by retaining repeating biological interactions while non-repeatable interactions disappear. (1) We define a species by its \textit{species interaction code}, which consists of a compact description of the repeating interactions of species organisms with their external and internal environments. Species interaction codes are descriptions recorded in the biological infrastructure that enables repeating interactions. Encoding and decoding are interwoven. (2) Evolution proceeds by natural autoencoding of sustained changes in species interaction codes. DNA is only one element in natural autoencoding. (3) Natural autoencoding accounts for the paradox of genome randomization in sexual reproduction -- recombined genomes are analogous to the diversified inputs required for artificial autoencoding. The increase in entropy generated by genome randomization compensates for the decrease in entropy generated by organized life. (4) Natural autoencoding and artificial autoencoding algorithms manifest defined similarities and differences. Recognition of the importance of fittedness could well serve the future of a humanly livable biosphere.