Maximizing Appropriate Responses Returned by a Conversational Agent through the Use of a Genetic Algorithm for Feature Selection.

We present an approach to creating conversational agents that are capable of returning appropriate responses to natural language input. The approach described consists of a genetic algorithm used as a feature selection technique to evolve a subset of random features towards a set of features that are more relevant to the language used in the domain; therefore improving the conversational agent's ability to return appropriate responses. The results show that over multiple iterations of the evolutionary process the genetic algorithm was able to filter out unfit features. After the evolutionary process the features that were found to be relevant were tested on an unseen test set and the algorithm achieved an accuracy of 72.678%