Hybrid fuzzy-genetic algorithm approach for crew grouping

Crew grouping is an important problem and formulating a good solution always involves many challenges. For example, grouping soldiers intelligently to tank combat units, we should take into consideration the combined technical proficiency of the soldiers, the amount of military training, the units from which the soldiers come, their service age, personal background, etc. In this paper we propose a hybrid Fuzzy-Genetic Algorithm (FGA) approach to solve the crew grouping problem. Fuzzy logic based controllers are applied to fine-tune dynamically the crossover and mutation probability in the genetic algorithms, in an attempt to improve the algorithm performance. The FGA approach is compared with the Standard Genetic Algorithm (SGA). Empirical results clearly demonstrates that while the SGA approach gives satisfactory solutions for the problem, the FGA method usually performs significantly better.