Emergence of Algorithmic Languages in Genetic Systems

TL;DR

This paper demonstrates that mutation and crossover in genetic systems promote the emergence of an algorithmic language that enhances the production of meaningful sequences, addressing the brittleness problem in genetic algorithms.

Contribution

It shows how an algorithmic language naturally emerges in genetic systems, facilitating meaningful genetic variation and overcoming brittleness, based on analysis of a neurogenetic model.

Findings

Emergence of an algorithmic language in genetic systems

Facilitation of meaningful sequence production

Reduction of brittleness in genetic algorithms

Abstract

In genetic systems there is a non-trivial interface between the sequence of symbols which constitutes the chromosome, or ``genotype'', and the products which this sequence encodes --- the ``phenotype''. This interface can be thought of as a ``computer''. In this case the chromosome is viewed as an algorithm and the phenotype as the result of the computation. In general only a small fraction of all possible sequences of symbols makes any sense for a given computer. The difficulty of finding meaningful algorithms by random mutation is known as the brittleness problem. In this paper we show that mutation and crossover favour the emergence of an algorithmic language which facilitates the production of meaningful sequences following random mutations of the genotype. We base our conclusions on an analysis of the population dynamics of a variant of Kitano's neurogenetic model wherein the chromosome encodes the rules for cellular division and the phenotype is a 16-cell organism interpreted as a connectivity matrix for a feedforward neural network. We show that an algorithmic language emerges, describe this language in extenso, and show how it helps to solve the brittleness problem.