The genetic algorithm can effectively handle some complex optimal problems that the conventional optimization methods can't solve. However, the traditional genetic algorithm has many defects, such as falling easily into local solution, slower convergence speed and the poor effect of optimal problems with constraints. An improved float-encoding genetic algorithm(FGA) solving optimal problems with inequality constraints is proposed. This method has the advantages of high convergence efficiency, good stability and strong local search capability. It is used to the optimal design of crank-link mech...
