Hardware implementation of a novel genetic algorithm
This paper introduces a novel genetic algorithm whose features have been purposely designed to be suited to hardware implementation. This is distinct from previous hardware designs that have been realized directly from conventional genetic algorithm approaches. To be suitable for hardware implementation, we propose that a genetic algorithm should attempt to both minimize final layout dimensions and reduce execution time while remaining a valid implementation. Consequently, the new genetic algorithm specifically aims to keep the requisite silicon area to a minimum by incorporating a monogenetic strategy that retains only the optimal individual, resulting in a dramatic reduction in the memory requirement and obviating the need for crossover circuitry. The results given in this paper demonstrate that new approach improves on a number of existing hardware genetic algorithm implementations in terms of the quality of the solution produced, the calculation time and the hardware component requirements.
genetic algorithms genetic hardware machine learning