Multilevel distributed genetic algorithms
Pavel Ošmera · 1995
Some problems are very difficult to solve by mathematical programming approaches. A genetic algorithm (GA) is an extremely powerful optimization technique that could be used to solve such problems, but its efficiency is dependent on its ability to do a large number of evaluations in a reasonable amount of time. A classical GA contains three basic operators-reproduction, crossover and mutation. To increase the efficiency of a genetic algorithm the influence of migration in a multilevel distributed GA (MDGA) was tested. Several different structures of PC computers connected in a local area network (LAN) were used for the MDGAs. MDGAs use the power of the computers better than one-level distributed GAs. The problem of communication between the computers in the MDGAs was dealt with in two different ways, with files on a server or by sending packets.