To overcome the defect that K2 algorithm requires the suitable order of nodes in advance while dealing with the structure learning of Bayesian Network Classifier (BNC), the algorithm GA-K2 is proposed which introduces the integer coding genetic algorithm based on selective ensemble concept to K2. It provides the guarantee of getting the best order of nodes and the convergence of Bayesian network structure for K2 in global optimization.
This paper uses the property of global optimization search and self-study ability of genetic algorithm,combined with the fuzzy control method,the intelligent variable frequency control method of Air-Condition based on genetic algorithm and fuzzy theory is researched.
It was observed that the SCE-UA method could locate the global optimum parameter set with 100% success rate indicating that SCE-UA is capable of finding the global optimum parameter set in the automatic calibration of the Xinanjiang model.
The mutated individual follows the advantage direction of current population, so it can jump local optimum and get the global optimum and it can avoid the semi-blind of conventional Darwinian-type evolutionary computation.
The global optimal solution is obtained and the calculation time is reduced by using a combination method of simulation annealing algorithm and a kind of direct optimization method (such as simplex method) for solving some nonlinear optimization problems.
An optimization algorithm based on genetic algorithm for parameters of synergetic neural network was proposed, which searched the optimal solutions in the parameters space of synergetic neural network using the globally optimal searching ability of genetic algorithm.
Based on the Kalman filtering method, applying the weighting measurement fusion method, a globally optimal multisensor measurement fusion Wiener signal filter is presented for single channel ARMA signals with white measurement noise.
Experimental results have shown that a global optimal solution can be quickly obtained using the proposed method and the precision requirement for target location is satisfied.
In addition, considering the non-convex and non-concave nature of the sub-problem of combinational optimization, the branch-and-bound technique was adopted to obtain or approximate a global optimal solution.
To speed up the search process and guarantee a global optimal result, the extended compact genetic algorithm (ECGA) is used to carry out the search process.
These schemes are based on a unified theoretical base-sufficient conditions for the global optimal known in optimal control theory.
2D and 3D ASMs are combined to obtain a "global optimal" segmentation of the 3D object embedded in the data set, rather than the "locally optimal" segmentation on separate slices.