INPUT: Size of each view L, P_c: probability of crossover, P_m: probability of mutation, K: number of views to be selected and G: the maximum number of generations

Core: Step 1: Initialization which generates randomly the population of the Top-K views as PTKV.

Step 2: Divide a PTKV into two equilibrium sub-populations PTKV1 and PTKV2 with size equal to PTKV/2.

Step 3: Compute the fitness for PTKV1 and PTKV2 using C_MV and C_NMV respectively as a two bi-objective optimization problem.

Step 4: Select the Top-K views from PTKV1 and PTKV2 using the proportionate selection based on the fitness of the Top-K views, and Compute subpopulation of Top-K views SPTKV1 and SPTKV2 using the corresponding objective function.

Step 5: Combine the Top-K views in SPTKV1and SPTKV2 and generate a mating population of Top-K views MPTKV.

Step 6: Apply the modified crossover and the random mutation operators to generate the offspring population of the Top-K views.

OUTPUT: Top-K views TKV