The strongest schema learning GA and its application to multilevel thresholding

摘要

The multilevel thresholding segmentation methods often outperform the bi-level methods. However, their computational complexity will also grow exponentially as the threshold number increases due to the exhaustive search. Genetic algorithms (GAs) can accelerate the optimization calculation but suffer drawbacks such as slow convergence and easy to trap into local optimum. Extracting from several highest performance strings, a strongest scheme can be obtained. With the low performance strings learning from it with a certain probability, the average-fitness of each generation can increase and the computational time will improve. On the other hand, the learning program can also improve the population diversity. This will enhance the stability of the optimization calculation. Experiment results showed that it was very effective for multilevel thresholding.