| Input: , |
| Output: Attribute reduction red |
| 1. Initialization: |
| 1.1 Calculate the dependency of the decision attribute on the condition attribute according to formula (3). |
| 1.2 Let , for any attributes , if , then ; if , then |
| is the minimum reduction in the base attribute for the condition attribute ; otherwise, step 3 is performed. |
| 1.3 For any attributes , if , then the corresponding chromosomal gene position is 1; else, the random selection |
| of 0 and 1 as their chromosomal gene is performed. |
| 2. Start the iterative process: |
| 2.1 According to formula (3), calculate the individual attribute dependency value, calculate the individual fitness value by formula |
| (4), and then sort the population in descending order according to the size of the fitness value. |
| 2.2 Select the first M different individuals to compose the elite library and let t = 0; from the population , select |
| the individuals to form subpopulations A and B according to formula (5). |
| 2.3 Subpopulation undergoes evolutionary operations: |
| (1) The elite algorithm assists in the crossover and randomly selects the elite individuals in the elite bank and the individuals in |
| the child population to complete the collaborative cross-operation |
| (2) Perform the mutation operation to obtain subpopulation . |
| 2.4 Evolution of subpopulation : If the number of iterations is higher than , generate random populations and perform elite |
| assisted crossover operations; otherwise, perform mutation operations to obtain subpopulation . |
| 2.5 Combine the populations and to obtain the population and calculate the fitness value of the |
| population . |
| 2.6 If has an individual fitness value greater than that of , replace with the smallest fitness value to obtain |
| and ) sorted in descending order. Take the first different individuals in |
| to update the elite library and get ; |
| 2.7 It is determined whether the termination condition is satisfied. If it is satisfied, it ends; otherwise, start over at 2.1. |
