EMERGENCE OF ALGORITHMIC LANGUAGE IN GENETIC SYSTEMS

In genetic systems there is a non-trivial interface between the sequen ce 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 view ed as an algorithm and the phenotype as the result of the computation. In general, only a small fraction of all possible sequences of symbol s makes any sense for a given computer. The difficulty of finding mean ingful algorithms by random mutation is known as the brittleness probl em. In this paper we show that mutation and crossover favor the emerge nce of an algorithmic language which facilitates the production of mea ningful 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 rul es for cellular division arid the phenotype is a 16-cell organism inte rpreted 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. (C) 1 998 Elsevier Science Ireland Ltd. All rights reserved.