|Budget Amount *help
¥2,200,000 (Direct Cost : ¥2,200,000)
Fiscal Year 1997 : ¥200,000 (Direct Cost : ¥200,000)
Fiscal Year 1996 : ¥300,000 (Direct Cost : ¥300,000)
Fiscal Year 1995 : ¥1,700,000 (Direct Cost : ¥1,700,000)
(1) In order to apply the genetic algorithm to real-scale practical optimization problems, the massively parallel model of genetic algorithm that can be efficiently implemented on a SIMD machine is investigated. The first candidate of the model, the farming model, uses a set of groups of individuals that do not exchange individuals each other as the well-known island model does, but exchange the best individual of the group with a master. Thus the most excellent individual is distributed to all groups rapidly. By piling up the duplicates of set of master and groups of individuals in a multilevel layr, the hierarchical massively parallel system is easily developed. To implement this model on FPGA chip as a SIAD machine, we have designed the principal elements of the machine by using the hardware description language VHDL.
(2) Intending to still increase the degree of parallelism, the massively parallel genetic algorithm model is selected as the second candidate. In this model, each indiv
idual takes the arbitrary individuals in the neighbor for the cross-over and the selection. The convergence of this model was confirmed by thorough simulation. To implement this algorithm on a SIMD machine, each processing element is assigned to each one of the individuals and those processing elements need to communicate with each other very frequently. Therefore the performance of interconnection network becomes very important. We have designed a high-speed router as a key element to develop the high-performance interconnection network to be used in this machine.
(3) The great feature of the SIMD machine is that many processing elements can be integrated on a single VLSI chip because of its simple structure. However, the programming flexibility of SIMD machine is known to be very poor so that it is used only for limited applications. Particularly, the searching programs such as that of genetic algorithm, is considered difficult to run on SIMD machine. On the other hand, the programming of MIMD machine is very flexible as it can use SPMD (single program multiple data) paradigm which is very akin to the sequential program. Should the SIMD machine can run the SPMD programs, the SIMD machine will be undoubtedly superior of all aprallel machine. We have investigated to find a way to this goal and have developed a new branching mechanism that can be added to the conventional SIMD machines. We have defined this mechanism clearly and designed the control processor as well as the processing element. Significant improvement in computational speed was observed when a searching program is assumed to be run on this machine. Less