In the past few decades, knowledge based techniques have made an impressive contribution to intelligent information retrieval (IR). These techniques stem from research on artificial intelligence, neural networks (NN) and genetic algorithms (GA) and are used to answer three main IR tasks: information modelling, query evaluation and relevance feedback. The paper describes IR approaches based on connectionist and genetic approaches. Our goal is to take benefits of these techniques to fulfill the user information needs. More precisely a multi-layer NN, Mercure, is used to represent the document space in an associative way, to evaluate the query using spreading activation and to implement a relevance feedback process by relevance back-propagation. Another query reformulation technique is investigated which uses the GA approach. The GA generates several queries that explore different areas of the document space. Experiments and results obtained with both techniques are shown and discussed.