An abstract model of nonrandom, non-Lamarckian mutation in evolution using a multivariate estimation-of-distribution algorithm

TL;DR

This paper introduces a simulation model based on estimation-of-distribution algorithms that demonstrates nonrandom, non-Lamarckian mutation influenced by internal genomic information, aligning with the Interaction-based Evolution theory.

Contribution

It presents a concrete simulation model showing how nonrandom, non-Lamarckian mutations can interact with selection and recombination, emphasizing internal information integration in evolution.

Findings

Mutations are influenced by internal genomic information rather than being purely random.

Evolution involves interaction of selection, recombination, and internally driven mutation.

The model connects evolutionary processes with computational learning theory concepts.

Abstract

At the fundamental conceptual level, two alternatives have traditionally been considered for how mutations arise and how evolution happens: 1) random mutation and natural selection, and 2) Lamarckism. Recently, the theory of Interaction-based Evolution (IBE) has been proposed, according to which mutations are neither random nor Lamarckian, but are influenced by information accumulating internally in the genome over generations. Based on the estimation-of-distribution algorithms framework, we present a simulation model that demonstrates nonrandom, non-Lamarckian mutation concretely while capturing indirectly several aspects of IBE: selection, recombination, and nonrandom, non-Lamarckian mutation interact in a complementary fashion; evolution is driven by the interaction of parsimony and fit; and random bits do not directly encode improvement but enable generalization by the manner in which they connect with the rest of the evolutionary process. Connections are drawn to Darwin's observations that changed conditions increase the rate of production of heritable variation; to the causes of bell-shaped distributions of traits and how these distributions respond to selection; and to computational learning theory, where analogizing evolution to learning in accord with IBE casts individuals as examples and places the learned hypothesis at the population level. The model highlights the importance of incorporating internal integration of information through heritable change in both evolutionary theory and evolutionary computation.