# An Efficient Information Retrieval System Using Evolutionary Algorithms
###### Abstract
Hence, finding the most satisfactory information from databases is increasingly challenging [2]. Data organization is required to conduct data update and query operations effectively. Indexing is one of the adopted methods, where different indexing techniques are being explored for the content [3]. Indexing techniques are essential to improve the dataset's performance and security. In contrast, information retrieval (IR) indices have a variety of drawbacks, including huge index sizes, the inability to accumulate an output search, and potential security risks. There are more motivations for this paper to develop the proposed system, i.e., generally, two main problems in IRSs are still encountered by the web user when trying to retrieve documents, which are related to the user query; one of these is that many of the retrieved documents are highly ranked and are not related to the request of the user. Additionally, the main information retrieval problem is determining the document's relevance to the user's requirements. Practically, this issue is considered a ranking problem that needs to be solved based on the matching between all documents and the user query dealing with information retrieval [5, 6]. The second problem is that many related documents are found in the dataset but are not retrieved [7, 8, 9, 10, 11, 12, 13]. The relevant documents can be found by generating an optimal query [14, 15, 16, 17, 18, 19]. An initial query is used with random or estimated weights, and new queries are generated based on the modification of the new weights. Based on the identified techniques, the new retrieval model is evaluated by comparing it with some existing models in a controlled environment. Thus, these approaches are still insufficient [20, 21, 22, 23]. Therefore, we propose the advanced document indexing method (ADIM) as a preprocessing stage that takes less time to build and produces a lower index size. After the modified genetic algorithm (MGA) was integrated with the culture algorithm (CA) for the first time, these two algorithms were adapted and developed to work in IRSs to retrieve relevant documents for user queries. Thus, the new indexing method was created on our proposed methods that can deal with huge datasets and respond to queries in near-linear time with little I/O overhead. To the best of the authors' knowledge, targeted results have not yet been achieved. However, the novel work of this paper uniquely contributes to solving these problems by:* Proposing a novel indexing technique called the advanced document indexing method (ADIM) applied to large IRS-indexed files joined with modified GA and CA for retrieving relevant documents to the user queries;
* Reducing the amount of storage required for the produced ADIM;
* Modifying genetic algorithm (MGA) and integrating with culture algorithm (CA) to retrieve relevant documents. The remainder of the paper structured as follows: Section 2 includes a background and literature review. Section 3 explains the details of the proposed system (methodology). Section 4 illustrates the proposed system implementation. Section 5 describes the discussion and analysis of the conducted results. Finally, Section 6 summarizes the conclusions and recommendations for further study.