Document categorisation by genetic algorithms

Today, it is easy to provide information to and retrieve information from the Internet. However, the problem of information overload has to be overcome. One of the main issues to be addressed for the information overload problem is document classification. We present an evolutionary approach to automatically categorize documents into appropriate categories. Our approach deals with different categories of documents separately: it evolves a numerical list that consists of the corresponding weights of the feature words for each class of documents. Experimental results show that our approach can easily evolve the classifiers of numerical lists, and that the evolved classifiers perform better than those constructed by the traditional k-nearest neighbors approach