科研之友
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摘要
In this paper, an evolutionary algorithm inspired by biological deoxyribonucleic acid (DNA) computing is proposed to perform the task of optimization. As a novel branch of computational intelligence, DNA computing has the strong computing and proliferating capability to in DNA strands by DNA encoding and matching in the molecule layer. However, it is difficult to apply DNA computing to search processing in a nonlinear hyperspace, because traditional DNA computing often relies on biochemical reactions of DNA molecules and may result in incorrect or undesirable computations. To utilize the advantages and avoid the problems of biological DNA computing, this article proposes an artificial DNA computing approach, which accelerates the process by means of the operation of restriction enzymes based on constructed schemata. After DNA encoding, the typical DNA database is constructed and evolved by evolutionary algorithms and the schema theory of genetic algorithm (GA) integrated to enhance the accuracy and convergence rate of exploration in the search space. Meanwhile, to fully utilize parallelism characteristics of DNA computing, a novel algorithm, named parallel DNA computing algorithm, which is tailored to be implemented in graphics processing unit devices, is proposed and exanimated its performance on benchmark optimization problems. Experimental results demonstrate that the proposed method presents excellent expected processing time efficiency, compared with its counterparts without the designed accelerating processes, such as schemata, restriction enzyme, and migration. As well, the performance is also distinctly superior to two renowned GAs.
- 出版日期2016
- 单位中山大学
