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Genetic algorithm as a powerful heuristic optimization algorithm excels in complex problems and global optimization. The aim of this study is to deeply explore the application of genetic algorithm in optimization problems and fully analyze its significance and effectiveness in several fields. Genetic algorithms represent solutions in the problem space as individuals by modeling the basic principles of biological evolution, and generate new generation solutions through crossover, mutation and other operations. Its application in complex problem solving is of wide significance, not only efficiently searching the problem space, but also dealing with complex situations such as multi-objectives and multi-constraints, which provides feasible optimization solutions for practical problems. The study adopts a typical genetic algorithm process, including individual representation, selection, crossover, mutation and other operations. By adjusting the algorithm parameters, adaptive optimization is achieved and the robustness of the algorithm is improved. In the example study, the optimal path is found by genetic algorithm, taking into account both path length and time constraints. Satisfactory results were obtained through the example study. The optimized path meets the minimization requirement of length, which proves the effectiveness of genetic algorithm in solving practical problems. The powerful performance of genetic algorithm in solving complex optimization problems is demonstrated, which provides useful insights for further research and application.
Hao et al. (Fri,) studied this question.