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Genetic algorithms are computer techniques inspired by the theory of evolution and genetics. Individual definition, chromosome initialization, chromosome testing, selection (crossover) and mutation are fundamental elements of genetic algorithms. Genetic algorithms are used to solve optimization problems, such as lesson planning, community services and traffic light adjustment. By producing the best combination of chromosomes, the genetic algorithm can achieve ideal results. The genetic algorithm produces appropriate planning data to avoid delays. This research uses the methods of data collection, individual definition and chromosome initialization. The result of this research is a service application designed to be able to plan efficiently through development using a genetic algorithm. Optimal planning occurs when processing planning data produces solutions that are efficient in terms of time, energy, and resources, and avoids conflicting schedules in the same place..

A network is a system in which computing devices are connected to each other and can share data and resources. To establish a connection, these network devices follow certain rules called communication protocols, both through physical and wireless technology. The performance level of a Wi-Fi network, for example inside a building, can be measured by how well users receive the Wi-Fi signal. The goal of this network is to establish timely and precise communication between users and data senders. A successful Internet connection often depends on receiving a strong and consistent signal, while a fluctuating or weak signal is undesirable. A commonly used approach to measure the average Wi-Fi signal level in a building without needing to understand every detail of the building layout is to use Single Slope Modeling (1SM). This model bases its calculations on the distance between the sending and receiving devices, so it can be used to analyze the performance of Wi-Fi networks, as is done in the case of educational institution buildings. Apart from that, in an effort to optimize network performance, a genetic algorithm was also created which was implemented using the PHP language. This algorithm is based on the principles of genetic algorithms to overcome optimization problems that may arise in the context of such networks.

In the package delivery industry, exemplified by J&T Express Manado, optimizing courier distribution routes is essential for customer satisfaction, cost reduction, and on-time deliveries. The Traveling Salesman Problem (TSP) is a valuable tool for finding efficient routes to visit all delivery points once. This study employed the Genetic Algorithm and Nearest Neighbor Algorithm to tackle the TSP, aiming to identify the shortest routes and minimize distribution distances for J&T Express Manado's couriers using geographical coordinates. The Genetic Algorithm resulted in a distribution route of 41.20678 km, while the Nearest Neighbor Algorithm achieved a shorter route of 38.10361 km. For J&T Express Manado, our findings indicate that the Nearest Neighbor Algorithm excels in identifying the shortest courier distribution route and requires significantly less computational time. This study offers insights for J&T Express Manado and similar courier services, enabling them to enhance distribution operations, potentially reducing costs and improving efficiency. It also underscores the practical advantages of the Nearest Neighbor Algorithm in addressing TSP challenges within the industry

The advent of the Internet as an effective means for resource sharing has consequently, led to proliferation of adversaries, with unauthorized access to network resources. Adversaries achieved fraudulent activities via carefully crafted attacks of large magnitude targeted at personal gains and rewards. With the cost of over $1.3Trillion lost globally to financial crimes and the rise in such fraudulent activities vis the use of credit-cards, financial institutions and major stakeholders must begin to explore and exploit better and improved means to secure client data and funds. Banks and financial services must harness the creative mode rendered by machine learning schemes to help effectively manage such fraud attacks and threats. We propose HyGAMoNNE – a hybrid modular genetic algorithm trained neural network ensemble to detect fraud activities. The hybrid, equipped with knowledge to altruistically detect fraud on credit card transactions. Results show that the hybrid effectively differentiates, the benign class attacks/threats from genuine credit card transaction(s) with model accuracy of 92%.

Quantum-Inspired Algorithms (QIAs) combine principles of quantum computing with classical evolutionary strategies to address complex optimization problems. This research explores the potential of QIAs in improving optimization processes, particularly in combinatorial and multi-objective optimization scenarios. The study focuses on the application of Quantum-Inspired Genetic Algorithms (QIGAs) and Quantum-Inspired Evolutionary Algorithms (QIEAs), assessing their effectiveness in solving classical problems like the Traveling Salesman Problem (TSP) and Minimum Spanning Tree (MST). Through computational simulations, the research compares the time convergence and solution accuracy of QIAs against traditional classical algorithms. The findings demonstrate that QIAs achieve faster convergence rates and higher-quality solutions, with accuracy levels reaching 98-99% of the global optimal solutions, while significantly reducing computational time. These results underline the advantages of QIAs in solving large and complex optimization problems, making them a promising alternative to traditional algorithms. Additionally, QIAs excel in avoiding local minima, a common pitfall of classical methods, due to their ability to explore the solution space more efficiently through quantum principles like superposition and interference. The implications of this study suggest that QIAs can be a valuable tool for tackling real-world optimization challenges, with potential applications in fields such as finance, logistics, telecommunications, and energy management. The research also indicates the necessity for further improvements in quantum-inspired algorithms' scalability and hardware integration, particularly for larger, more intricate optimization problems, to fully realize their potential in practical industrial applications.

Travelling Salesman Problem (TSP) is a problem that is often encountered by a salesman who must travel exactly once to all consumers in a route and will return to the starting point of departure. Algorithm Genetic Algorithm is one way to find heuristic solutions based on the evolutionary ideas of natural selection and genetics. The aim is to find the optimal route for the distribution of bottled water products produced by PT. Mual Natio Maju Bersama. To find a solution, the chromosomes processed by the genetic algorithm are represented through the stages in the Genetic Algorithm individual initialization, fitness value, linear fitness ranking, roulette whell selection, crossover, and mutation. In order to achieve the optimum solution, namely The best path obtained is PT Mual Tio Maju Bersama –BUMDES Sait ni Huta - UD. Alvaro - UD. Lancelhot – UD. Alris – UD. Jamel – Toko Kelontong SRC Resi 2 – Toko Notra – UD. B Siringoringo – Toko Dahlia Siahaan – UD. Purba – UD. Cahaya – UD. Hutapea – UD. Gabe – UD. Setia II – UD. Larisma II – UD. Antoni – UD. Bona Siahaan – UD. Sederhana – Toko Manalu – UD. Setia I – Toko Ferdinan – UD. Alboy – Wisma Daun Mas – UD. Top Jaya – UD. Mega Silaban – BUMDES Silaitlait – UD. Rika – UD. Panamot – Piltik Coffee and Homestay Bandar Udara Silangit – UD. Rolas Boy – UD. Salamat Karya – UD. Simpang Jaya – UD. Lambok - Piltik Coffee and Homestay Siborongborong – UD. Bahagia – UD. Marlinca – UD. Heri Joel Pasaribu – UD. Ebenezer – UD. Mawar – UD. A Saudara – UD. SP Perdana – PDAM Mual Na Tio – UD. Rokkap - PT Mual Tio Maju Bersama. The best path length is 125.2700 cartesian units and the best fitness value is 0.008000.