Genetic Algorithm for Path Planning with Time Windows

Genetic Algorithm (GA) is a computational intelligence technique that mimics natural selection and genetic mechanisms to find optimal solutions. In path planning problems with time windows, GA encodes each route's characteristics - including path length, time window constraints, and other limitations - into chromosome representations. The algorithm typically implements key operators such as selection, crossover, and mutation to evolve population solutions across generations. Fitness functions evaluate solutions based on objective criteria like total distance traveled and time window adherence. For implementation, the genetic algorithm workflow includes population initialization, where routes are represented as gene sequences; fitness calculation using constraint-handling techniques; and genetic operations that maintain solution diversity. This approach provides feasible solutions for large-scale problems and continuously optimizes results through evolutionary improvement. The method proves particularly effective for complex routing scenarios where traditional algorithms struggle with multiple constraints, making genetic algorithm a robust and practical solution for time-constrained path planning challenges.