Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Proceedings of the 5th International Conference on Genetic Algorithms
Structure and Performance of Fine-Grain Parallelism in Genetic Search
Proceedings of the 5th International Conference on Genetic Algorithms
Serial and Parallel Genetic Algorithms as Function Optimizers
Proceedings of the 5th International Conference on Genetic Algorithms
Development of a customized processor architecture for accelerating genetic algorithms
Microprocessors & Microsystems
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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 roulette-wheel 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 against PGA (Parallel Genetic Algorithms) shows that PLGA may be even more effective than PGAs. A scheme for realizing the hardware pipeline is also presented. Since a general function evaluation unit is essential, a detailed description of one such unit is presented.