Adaptive operator selection for path planning in static environments

Genetic algorithms have been fairly successful in solving difficult and ill-behaved optimization problems, such as path planning. In this paper we use the operator selection approach and introduce new one operator to solve the path planning problem. The Adaptive Operator Selection is utilized in the genetic algorithm to select the operators optimally in a given situation. In this paper, two different probability-based methods, namely the Probability Matching and the Adaptive Pursuit are used for this purpose. The methods are implemented and simulated in several static environments.