Search dynamics of fitness landscape learning evolutionary computation with two types of evolution control

Fitness approximation methods in Evolutionary Computation (EC) provide us good results in real-world optimization. On the other hand, little is known about the advantages and disadvantages of each surrogate models. Moreover, the performance of models depends on a structure of original function. Therefore, various kinds of surrogate models can leads to better results. We also have proposed a novel surrogate model which can estimate the only rank of two individuals using Support Vector Machine. In addition, we have proposed EC framework with that model called Fitness Landscape Learning Evolutionary Computation (FLLEC) which has shown good performance. In this paper, we compared two type of evolution control in FLLEC with the computational experiments.