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Emergence of algorithmic language in genetic systems

Authors
Journal
Biosystems
0303-2647
Publisher
Elsevier
Publication Date
Volume
47
Issue
3
Identifiers
DOI: 10.1016/s0303-2647(98)00007-0
Keywords
  • Emerging Properties
  • Neurogenetic Model
  • Genotype–Phenotype Relation
  • Algorithmic Language
  • Genetic Symmetry Breaking
  • Adaptive Evolution
Disciplines
  • Biology
  • Computer Science

Abstract

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 favor 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 feed-forward neural network. We show that an algorithmic language emerges, describe this language in extenso, and show how it helps to solve the brittleness problem.

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