A hardware pipeline for function optimization using genetic algorithms

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. In this paper, a pipelined version of the commonly used Genetic Algorithms and a corresponding hardware platform 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 modified algorithm is termed PLGA (Pipelined Genetic Algorithm). When executed in a CGA (Classical Genetic Algorithm) framework, the stochastic selection gives comparable performances with the roulettewheel selection. In the pipelined hardware environment, PLGA will be 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 again...

• Type: Conference
• Year: 2005
• Where: GECCO
• Authors: Malay Kumar Pakhira, Rajat K. De
• Keywords: GECCO 2005, Genetic Algorithms, PLGA, Stochastic Selection