A fuzzy genetic algorithm with effective search and optimization

A fuzzy genetic algorithm (FGA) is created by systematically integrating fuzzy expert systems (FESs) with genetic algorithms in this paper, with the goal of this integration being the synergism of their advantages and strengths. In the FGA, FESs can model expert knowledge for genetic algorithms on the specific tasks being addressed. They can also assist in initial selection and dynamic online adjustment of the control parameters of the algorithms, resulting in significant improvement in FGA´s search and optimization efficiency. Experiments demonstrate that FGAs can search faster and more effectively than standard genetic algorithms in solving the traveling salesman and other optimization problems.