Function Optimization Using a Pipelined Genetic Algorithm

Abstract

Genetic algorithms (GAs) are very commonly used as function optimizers, basically due to their search capability. A number of different serial and parallel versions of GA exist. A pipelined version of a commonly used genetic algorithm is described. The main idea of achieving pipelined execution of different operations of GA is to use a stochastic selection function which works with the fitness value of the candidate chromosome only. The GA with this selection operator is termed PLGA. When executed in a CGA (classical genetic algorithm) framework, the stochastic selection scheme gives performances comparable with the roulette-wheel selection. In the pipelined hardware environment, PLGA is much faster than the CGA. When executed on similar hardware platforms, PLGA may attain a maximum speedup of four over CGA. However, if CGA is executed in a uniprocessor system, the speedup is much more. A comparison of PLGA against PGA (parallel genetic algorithms) is also done.