3.2. Heuristics and metaheuristics
Due to the NP-hardness of the maintenance problems faced in
power systems and the size of real-word instances, heuristic techniques
have been largely developed. These methods also allow for
more flexibility to deal with non linear or very complex constraints
and/or objectives.
3.2.1. Genetic algorithms
GAs are widely used for solving the GMS problem due, mainly, to
the structure of the decisions that are made. These algorithms are inspired
by natural evolution. A population of abstract representations
of solutions, called individuals, evolves through an iterative process
toward better solutions. Solutions are usually encoded to facilitate
the application of the several techniques used in GAs such as selection,
mutation and crossover. In the particular case of GSM, different