文档介绍：A Survey of Parallel Distributed ic Algorithms 5. Classification of Parallel and Sequential GAs 6. Technical Issues in Parallel Distributed GAs 7. Implementation Issues 8. Concluding Remarks Par. dGA Ref. Year Main Characteristics PGA [55] 1987 Generational islands on an Intel iPSC hypercube (8 CPUs). Migrate the best. Dynamic Top. dGA [68] 1989 Distributed populations. Good results with 20% of population migrants every 20 generations GENITOR II [73] 1990 Steady-State islands with ranked selection and reduced surrogate crossover PGA [51] 1991 Sub-populations ina circular ladder-like 2-D topology. Migrate the best, local hill-climbing SGA-cube [23] 1991 Made for nCUBE2. This is the parallel extension of the well-known simple GA of Goldberg PARAGENESIS ---1992 Made for the CM-200. This places one individual in every CPU PeGAsuS [59] 1993 Targeted for MIMD machines and written ina very high and flexible description language GAMAS [56] 1994 Uses 4 very heterogeneous species (islands) and quite specialized migrations and genotypes iiGA [44] 1994 Injection island GA with hierarchical heterogeneous nodes and asynchronous migrations SP1-GA [42] 1994 128 steady-state islands on an IBM SP1 machine of128 nodes. 2-D toroidal mesh. mr=1 DGENESIS [47] 1994 Free topology, flexible migration, and policies for selection. Implemented with sockets (UDP) GALOPPS [30] 1996 Very flexible. Implemented with PVM prising a large number of operators GDGA [34] 1996 Synchronous. Simulated onone processor. Generational. Uses Fuzzy crossover and FP genes CoPDEB [2]1996 Every island uses its own probabilities for mutation, crossover, and specialized operators of Parallel and Sequential GAs TABLE Ⅲ OVERVIEW OF PARALLEL DISTRIBUTED GAs BY YEAR of Parallel and Sequential GAs Reference Application Domain [7] Parallel training of artificial works, fuzzy logic controllers, munication protocols [19] Synthesis of VLSI circuits [31] Function optimization [42] Set