  Feature selection plays a vital role in promoting the classifier's
performance. However, current methods ineffectively distinguish the complex
interaction in the selected features. To further remove these hidden negative
interactions, we propose a GA-like dynamic probability (GADP) method with
mutual information which has a two-layer structure. The first layer applies the
mutual information method to obtain a primary feature subset. The GA-like
dynamic probability algorithm, as the second layer, mines more supportive
features based on the former candidate features. Essentially, the GA-like
method is one of the population-based algorithms so its work mechanism is
similar to the GA. Different from the popular works which frequently focus on
improving GA's operators for enhancing the search ability and lowering the
converge time, we boldly abandon GA's operators and employ the dynamic
probability that relies on the performance of each chromosome to determine
feature selection in the new generation. The dynamic probability mechanism
significantly reduces the parameter number in GA that making it easy to use. As
each gene's probability is independent, the chromosome variety in GADP is more
notable than in traditional GA, which ensures GADP has a wider search space and
selects relevant features more effectively and accurately. To verify our
method's superiority, we evaluate our method under multiple conditions on 15
datasets. The results demonstrate the outperformance of the proposed method.
Generally, it has the best accuracy. Further, we also compare the proposed
model to the popular heuristic methods like POS, FPA, and WOA. Our model still
owns advantages over them.
