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Natural resource management involves complex decision-making processes that often result in non-linear optimization problems. This study explores the application of genetic algorithms (GA) and particle swarm optimization (PSO) to manage resources like water and forest reserves more efficiently. We compare the effectiveness of these algorithms in achieving sustainable utilization while minimizing environmental impact. The results show that GA outperforms PSO in forest management scenarios, while PSO is more suitable for water resource distribution.

This paper explores the use of genetic algorithms (GAs) for optimizing nonlinear systems in resource allocation. By simulating various allocation scenarios, we demonstrate the efficiency of GAs in finding near-optimal solutions in complex environments. The study provides a comparison of GA performance against traditional optimization methods and identifies scenarios where GAs outperform. The results emphasize the utility of GAs in real-world applications, especially when conventional approaches struggle with large solution spaces.

This paper introduces a hybrid optimization approach that combines genetic algorithms with gradient descent for effective nonlinear function approximation in highdimensional data. Traditional methods struggle with computational efficiency and accuracy in such complex spaces. By integrating genetic algorithms to provide a global search strategy with gradient descent for finetuning, the proposed method achieves faster convergence and improved accuracy. Simulations and case studies demonstrate its effectiveness in applications like data mining, image recognition, and financial modeling.

Labor scheduling is allocating human resources to work stations according to needs. To increase productivity, companies must schedule labor optimally. In allocating labor, the Hotel Menara Lexus company has implemented labor scheduling with 3 work shifts, namely morning, afternoon and night. Because scheduling is still made manually, it is possible for schedule "collisions" to occur and there is no fairness in the distribution of shifts to each employee. To solve this problem, an appropriate algorithm is needed so that the scheduling process can run optimally. The application of genetic algorithm methods in scheduling problems is able to produce good solutions using chromosome representation, determining objective function values, fitness evaluation, determining probability function values, selection process using Roulette Wheel, crossover method using one cut-point crossover, and mutation at the above level. 0.5 can then be generated. From parameter testing, the results obtained  

This paper presents an optimization approach for a hybrid photovoltaic (PV)-wind-battery energy system tailored for remote areas in Indonesia. Due to the archipelagic nature of the country, many remote areas lack access to the national power grid, making renewable energy solutions crucial. This study utilizes a genetic algorithm to optimize system configurations, balancing energy production, storage requirements, and cost efficiency. Results indicate that the proposed hybrid system can meet local demand with high reliability and at lower costs compared to diesel generators. The study also addresses environmental and social impacts, proposing sustainable energy strategies for Indonesia's underserved regions.