Comparative Studies of Fuzzy Genetic Algorithms
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Part II--Advances in Neural Networks
Expert Systems with Applications: An International Journal
Genetic algorithm: a seesaw method for generating offspring
MIC '07 Proceedings of the 26th IASTED International Conference on Modelling, Identification, and Control
Dynamic evolution of the genetic search region through fuzzy coding
Engineering Applications of Artificial Intelligence
Bio-inspired multi-agent systems for reconfigurable manufacturing systems
Engineering Applications of Artificial Intelligence
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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.