A genetic algorithm is a search technique used in computer science to find approximate solutions to optimization and search problems. Genetic algorithms are a particular class of evolutionary algorithms that use techniques inspired by evolutionary biology such as heredity, mutation, natural selection, and crossing over. Genetic Algorithms can also be defined as global optimization algorithms and model a solution to a specific problem by creating a data structure like that of a chromosome and applying operators that recombine these structures while preserving optimal information. The implementation of the genetic algorithm begins with a population of chromosomes. These structures are evaluated to generate reproductive opportunities where the chromosomes that represent a better solution are more likely to reproduce than those representing a worse solution. The definition of a better or worse solution is usually related to the current population. This topic will continue with the application of the concepts under study through the MATLAB environment, seeking improved solutions in relation to the results already known. In the area of celestial mechanics, the effects of dissipative forces in the orbital transfer environment will be investigated, having as main effects the gravitational pull of the Planet and the pressure force of solar radiation. The effects and variations caused by this disturbance in the orbits of terrestrial satellites and their possible consequences and variations over time are analyzed. The effects will be computed as a result of the integration of the satellite motion equations and the analysis of the effects of the disturbance on the satellite orbit. As a result, the relevance of the perturbative model to the importance of this treatment is expected due to the precision of the results through this modeling. In this work we will discuss the optimization of space trajectories in missions to reduce mission costs using traditional methods and genetic algorithm for comparison purposes. The analysis and comparison of the genetic algorithm for optimization will be the main objective.
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