A shared-memory ACO+GA hybrid for combinatorial optimization
Most approaches used in EAs for problems in which the environment changes from time to time, try to preserve the diversity of the population. One of those approaches applies a new biologically inspired genetic operator called transformation (Simões & Costa, 2001). We tested two EAs using transformation (TGA and ETGA) and two other classical approaches: random immigrants (RIGA) and hypermutation (HMGA). The comparative study was made using the dynamic 0/1 Knapsack optimization problem.