In this paper we proposed an evolutionary approach for answering open-domain factoid questions, which include searching among sentences that are candidate for the final answer with Memetic Algorithm (MA), and using lexical and syntactic features for calculating fitness of the sentences. Our main purpose is making a search engine with accurate answering ability, or a web-based Question Answering (QA) system. The Text Retrieval Conference (TREC) QA Tracks data are used to develop and evaluate the approach. The answering process begins with retrieving related documents from a search engine. Then, MA searches among all the sentences of these documents and finds the best one. Finally, one or more words will be extracted based on our hand-made patterns. The results of different approaches for local search, mutation, and crossover, and also different values for number of reproduction and retrieved documents are investigated in the empirical study section. The results are promising with sufficient retrieved documents, and we have obtained a threshold value for this variable. Using MA instead of examining all the sentences is a trade-off between lowering the process time and sacrificing the accuracy, but the results show that the Mametic-based approach is more efficient.

Question answering (QA) is the task of automatically answering a question posed in natural language. At this time, there exists several QA approaches, and, according to recent evaluation results, most of them are complementary. Some of them use the evolutionary algorithms, such as the genetic algorithm, in itself. In this paper we propose a question answering system that uses the artificial immune algorithms, for searching in the knowledge base to find the right answer. This algorithm is one of the evolutionary algorithms. Search is based on two features: (i) the compatibility between question and answer types, (ii) the overlap and non-overlap information between the question-answer pair. Experimental results are encouraging; they indicate significant increases in the accuracy of proposed system, in comparison with the previous systems.