Although a genetic algorithm is most likely not the way language is
acquired by humans, genetic algorithms provide a way for a computer
program to "learn" a natural language. Given the Principles and
Parameters theory in linguistics, the vital information about a natural
language can be encoded as parameter settings. A genetic algorithm can
search for the parameter settings for a particular language, given input
sentences from that language. Once the parameter settings are found, the
program is well on the way to being able to understand the language.  In
my thesis, I present a very simple example of how a genetic algorithm
can be used to search for parameter settings of English. Only word order
parameters are used and they are kept necessarily simple, sacrificing
linguistic reality. The genetic algorithm itself is also kept very
simple, only using reproduction, mating, and mutation. The results of
running the genetic algorithm on input sentences are mixed. The genetic
algorithm itself works well, while randomness, the input, and the choice
of parameters cause multiple solutions to be found, where theoretically
there should only be one. A better parameter system and better input is
needed to solve the problem of having multiple solutions for a single
language. I think the results show that the overall concept of using a
genetic algorithm to set language parameters works and that it is the
linguistics that needs more work.
