# An Efficient Information Retrieval System Using Evolutionary Algorithms
## 2 Background and Literature Review
### Evolutionary Models (Search Techniques) Overview
#### 2.2.1 Genetic Algorithms (GA) Overview and Related Work  
The group of mathematical models known as GA is based on the ideas of natural selection and evolution. This heuristic is frequently employed to produce helpful optimization and look for answers to issues. GAs build chromosomes using selection factors, recombination, and mutations to develop chromosomes from selected problems using chromosome-like data. Typically, a randomized set of chromosomes is used to start the GA process. These chromosomes symbolize a challenge that needs to be overcome. Depending on the characteristics of the problems, each chromosome's positions are re-coded with numerical, alphabetic, or bit values. The positions denote genes; they change randomly throughout the development process. A population is a group of chromosomes present during the evolutionary stage. The evaluation function determines each chromosome's validation. There are two main considerations while evaluating something. In order to imitate population transformation and natural breeding, the crossover is performed. Chromosomal selection favors the fittest chromosomes for survival and synthesis. In the form of partial functions or by utilizing various chromosome-coding schemes created expressly for a given issue, GAs can virtually mimic any type of limitation. Figure 2 depicts a simple GA structure. Figure 2: Genetic algorithm simple structure. \begin{table}
\begin{tabular}{c c} \hline \hline
**Searching Technique** & **General Description** & **Disadvantages** \\ \hline Brute Force & It is a well-known technique, itemizing all potential participants to the resolution, determining whether each participant reveals problem statements. \\ \multirow{5}{* Binary} & It finds the position of a particular input value (i.e., the search key) within an array sorted by some key value. The given array needs to be arranged in an ascending or descending order. \\ \cline{1-1}  & The middle element key value of the provided arranged array is compared to the search key value using this technique. When both keys’ values matched, a matching item is discovered and indexed. \\ \hline \hline \end{tabular}
\end{table} 
Table 3: _Cont._Many publications have introduced advanced interactive genetic algorithms (IGA) to improve IR. [PERSON] et al. [32] presented sparse fitness evaluation with an interactive genetic algorithm (IGA) to reduce the user's burden. Clustering was adopted to split the population into multiple subpopulations. A representative individual was selected from a subpopulation, where all fitness values of the remaining individuals were determined based on the representative's value and distance. [PERSON] et al. applied sparse fitness using 100 population sizes and 10 clusters. A different IGA using a paired comparison (PC-IGA) was implemented by [PERSON] et al. [33]. It permitted the required user to compare two individuals and select the best. The selected individual still competes until the best individual is obtained. The experimental results reached 7000 generations with different values of fitness function (FF). Whereas IGA based on fuzzy logic was suggested by [PERSON] et al. [34], the paper presents individuals' fuzzy and stochastic fitness for replacing the individual user's evaluation. It reduces users' burden by using a fuzzy number for fitness allocation. Furthermore, [PERSON] et al. suggested building a surrogate model to evaluate individual fitness to improve IEC or to replace user evaluation with different ranking distances (i.e., 10 and 20), and different values of FF. Additionally, [PERSON] et al. [35] proposed the IGA using the support vector machine, which is used to construct the classifier taking advantage of the initial user's selected examples that method reduced user fatigue when using a 250 population size. The experimental results reached 94% recall, and the time of execution took many hours. Moreover, a model of adaptive learning evaluation to assess beauty in the evolutionary art system was proposed by [PERSON] et al. [36].