# An Efficient Information Retrieval System Using Evolutionary Algorithms
###### Abstract  
When it comes to web search, information retrieval (IR) represents a critical technique as web pages have been increasingly growing. However, web users face major problems; unrelated user query retrieved documents (i.e., low precision), a lack of relevant document retrieval (i.e., low recall), acceptable retrieval time, and minimum storage space. This paper proposed a novel advanced document-indexing method (ADIM) with an integrated evolutionary algorithm. The proposed IRS includes three main stages; the first stage (i.e., the advanced documents indexing method) is preprocessing, which consists of two steps: dataset documents reading and advanced documents indexing method (ADIM), resulting in a set of two tables. The second stage is the query searching algorithm to produce a set of words or keywords and the related documents retrieving. The third stage (i.e., the searching algorithm) consists of two steps. The modified genetic algorithm (MGA) proposed new fitness functions using a cross-point operator with dynamic length chromosomes with the adaptive function of the culture algorithm (CA). The proposed system ranks the most relevant documents to the user query by adding a simple parameter (\(\infty\)) to the fitness function to guarantee the convergence solution, retrieving the most relevant user's document by integrating MGA with the CA algorithm to achieve the best accuracy. This system was simulated using a free dataset called WebKb containing Worldwide Webpages of computer science departments at multiple universities. The dataset is composed of 8280 HTML-programed semi-structured documents. Experimental results and evaluation measurements showed 100% average precision with 98.5236% average recall for 50 test queries, while the average response time was 00.46.74.78 milliseconds with 18.8 MB memory space for document indexing. The proposed work outperforms all the literature, comparatively, representing a remarkable leap in the studied field. c 2  
\"unstructured.\" This is true of all text data if you count the concealed linguistic structure of human languages. Most text, which is represented in documents by explicit markup (such as the coding underlying web pages), has structure, such as headings, paragraphs, and footnotes. For \"semi-structured\" information such as a document where the title contains Java and the body contains threading, the IRS is also used to facilitate the search for information type [1]. One goal of the IRS, which has special importance, is to provide the requirements of users with the most relevant documents. Different techniques and models were designed to achieve this important goal [2]. One of the main tasks in IR is classification, which is a characteristic that is suitable for machine learning. Learning algorithms use examples, attributes, and values, which IRSs supply in great quantities. This system includes three areas: symbolic learning, neural networks, and evolutionary computation-based algorithms [3, 4]. Some of the most widely used learning-based IR models are evolutionary computing algorithms such as genetic algorithms (GA) [3]. In addition, a universal repository of knowledge and culture in the digital world has allowed the direct sharing of ideas and information at an unpredictable rate. So, there is a need to access the digital world's data in the form of documents. These documents are useful for sharing information with every user, which is considered information retrieval. It is a computerized process of producing relevance-ranked list documents based on an inquirer's request [1]. Therefore, in recent years, using textual datasets to speed up the execution of critical tasks has been part of many people's and organizations' daily routines; most of the datasets include a massive collection of documents from a variety of sources, where datasets could be research papers, articles, news, digital libraries, books, messages, e-mails, or web pages. Due to the continuous development of information technology, the huge amount of information (datasets) is growing.