You are presented with multiple paper abstracts, alongside the associated literature review and chapter titles. Given the provided information, you are going to do a literature review. Please generate a coherent literature review that synthesizes the content, draws connections between different works, highlights major findings, and identifies any gaps in the existing research.

Literature Review Title: The Challenge of Non-Technical Loss Detection using Artificial Intelligence: A Survey 
Chapter Title:  D. Genetic algorithms 
1. Abstract of Cited Paper (BIB001):  Efficient methods for detecting electricity fraud has been an active research area in recent years. This paper presents a hybrid approach towards non-technical loss (NTL) analysis for electric utilities using genetic algorithm (GA) and support vector machine (SVM). The main motivation of this study is to assist Tenaga Nasional Berhad (TNB) in Malaysia to reduce its NTLs in the distribution sector. This hybrid GA-SVM model preselects suspected customers to be inspected onsite for fraud based on abnormal consumption behavior. The proposed approach uses customer load profile information to expose abnormal behavior that is known to be highly correlated with NTL activities. GA provides an increased convergence and globally optimized SVM hyper-parameters using a combination of random and prepopulated genomes. The result of the fraud detection model yields classified classes that are used to shortlist potential fraud suspects for onsite inspection. Simulation results prove the proposed method is more effective compared to the current actions taken by TNB in order to reduce NTL activities. 
2. Abstract of Cited Paper (BIB002):  Electricity consumer dishonesty is a problem faced by all power utilities. Finding efficient measurements for detecting fraudulent electricity consumption has been an active research area in recent years. This paper presents a new approach towards nontechnical loss (NTL) detection in power utilities using an artificial intelligence based technique, support vector machine (SVM). The main motivation of this study is to assist Tenaga Nasional Berhad (TNB) Sdn. Bhd. in peninsular Malaysia to reduce its NTLs in the distribution sector due to abnormalities and fraud activities, i.e., electricity theft. The fraud detection model (FDM) developed in this research study preselects suspected customers to be inspected onsite fraud based on irregularities in consumption behavior. This approach provides a method of data mining, which involves feature extraction from historical customer consumption data. This SVM based approach uses customer load profile information and additional attributes to expose abnormal behavior that is known to be highly correlated with NTL activities. The result yields customer classes which are used to shortlist potential suspects for onsite inspection based on significant behavior that emerges due to fraud activities. Model testing is performed using historical kWh consumption data for three towns within peninsular Malaysia. Feedback from TNB Distribution (TNBD) Sdn. Bhd. for onsite inspection indicates that the proposed method is more effective compared to the current actions taken by them. With the implementation of this new fraud detection system TNBD's detection hitrate will increase from 3% to 60%.

Literature Review:
The work in BIB001 and BIB003 is extended by using a genetic SVM in BIB002 for 1,171 customers. It uses a genetic algorithm in order to globally optimize the hyperparameters of the SVM. Each chromosome contains the Lagrangian multipliers (α 1 , ..., α i ), regularization factor C and Gaussian kernel parameter γ. This model achieves a test recall of 0.62. A data set of~1.1M customers is used in  . The paper identifies the much smaller sample of inspected customers as the main challenge NTL detection. It then proposes stratified sampling in order to increase the number of inspections and to minimize the statistical variance between them. The stratified sampling procedure is defined as a non-linear restricted optimization problem of minimizing the overall energy loss due to electricity theft. This minimization problem is solved using two methods: (1) genetic algorithm and (2) simulated annealing. The first approach outperforms the other one. Only the reduced variance is reported, which is not comparable to the other research and therefore left out of this survey.