Language Evolution and Computation Bibliography

This paper describes a study in the use of digital evolution to produce cooperative communication behavior in a population of digital organisms. The results demonstrate that digital evolution can produce organisms capable of distributed problem solving through interactions between members of the population and their environment. Specifically, the organisms cooperate to distribute among the population the largest value sensed from the environment. These digital organisms have no 'built-in' ability to perform this task; each population begins with a single organism that has only the ability to self-replicate. Over thousands of generations, random mutations and natural selection produce an instruction sequence that realizes this behavior, despite continuous turnover in the population.

We investigate common design decisions for constructing a computational genetic language in an autoadaptive system. Such languages must support self-replication and are typically Turing-complete so as not to limit the types of computations they can perform. We examine the importance of using templates to denote locations in the genome, the methods by which those templates are located (direct-matching versus complementmatching), methods used in the calculation of genome length and the size and complexity of the language. For each test, we examine the effects on the rate of evolution of the populations and isolate those factors that contribute to it, most notably the organisms' ability to withstand mutations.