A novel evolutionary method that allows us to study the emergence of modularity for genotype-phenotype mapping in the course of Darwinian evolution is described. The method is based on composite epigenotypes with two parts: a binary genotype; and a mapping of genes onto phenotype characters. For such generalized epigenotypes the modularity is determined in the following intuitive way: The genes are divided into two subgroups; simultaneously with this decomposition there is defined an accompanying decomposition of the set of phenotype characters. We expect that for epigenotypes with modular structures the genes from one group will be mapped onto characters from the same group, that is, that the appearance of crosslink mappings will be maximally suppressed. A fundamental question for all of evolutionary biology (and also for evolutionary algorithms and connectionist cognitive science) is the mechanism of evolutionary emergence of modular structures. The presented explanatory model is an implementation of the assumption that variation in genotype is produced on a faster time scale than variation in the genotype-phenotype mapped part. Moreover, the evaluation of the epigenotype in the evolutionary algorithm is based on directly selectable properties (corresponding to the decomposition of the set of phenotype characters). The modularity of genotypephenotype mapping emerges in the simulations.

The purpose of this article is to demonstrate that coordinated communication spontaneously emerges in a population composed of agents that are capable of specific cognitive activities. Internal states of agents are characterized by meaning vectors. Simple neural networks composed of one layer of hidden neurons perform cognitive activities of agents. An elementary communication act consists of the following: (a) two agents are selected, where one of them is declared the speaker and the other the listener; (b) the speaker codes a selected meaning vector onto a sequence of symbols and sends it to the listener as a message; and finally, (c) the listener decodes this message into a meaning vector and adapts his or her neural network such that the differences between speaker and listener meaning vectors are decreased. A Darwinian evolution enlarged by ideas from the Baldwin effect and Dawkins' memes is simulated by a simple version of an evolutionary algorithm without crossover. The agent fitness is determined by success of the mutual pairwise communications. It is demonstrated that agents in the course of evolution gradually do a better job of decoding received messages (they are closer to meaning vectors of speakers) and all agents gradually start to use the same vocabulary for the common communication. Moreover, if agent meaning vectors contain regularities, then these regularities are manifested also in messages created by agent speakers, that is, similar parts of meaning vectors are coded by similar symbol substrings. This observation is considered a manifestation of the emergence of a grammar system in the common coordinated communication.