Vol.67, No.3, 2021, pp.3293-3310, doi:10.32604/cmc.2021.015495
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ARTICLE
Parallel Optimization of Program Instructions Using Genetic Algorithms
  • Petre Anghelescu*
Department of Electronics, Communications and Computers, University of Pitesti, Pitesti, 110040, Romania
* Corresponding Author: Petre Anghelescu. Email:
Received 24 November 2020; Accepted 12 January 2021; Issue published 01 March 2021
Abstract
This paper describes an efficient solution to parallelize software program instructions, regardless of the programming language in which they are written. We solve the problem of the optimal distribution of a set of instructions on available processors. We propose a genetic algorithm to parallelize computations, using evolution to search the solution space. The stages of our proposed genetic algorithm are: The choice of the initial population and its representation in chromosomes, the crossover, and the mutation operations customized to the problem being dealt with. In this paper, genetic algorithms are applied to the entire search space of the parallelization of the program instructions problem. This problem is NP-complete, so there are no polynomial algorithms that can scan the solution space and solve the problem. The genetic algorithm-based method is general and it is simple and efficient to implement because it can be scaled to a larger or smaller number of instructions that must be parallelized. The parallelization technique proposed in this paper was developed in the C# programming language, and our results confirm the effectiveness of our parallelization method. Experimental results obtained and presented for different working scenarios confirm the theoretical results, and they provide insight on how to improve the exploration of a search space that is too large to be searched exhaustively.
Keywords
Parallel instruction execution; parallel algorithms; genetic algorithms; parallel genetic algorithms; artificial intelligence techniques; evolutionary strategies
Cite This Article
P. Anghelescu and . , "Parallel optimization of program instructions using genetic algorithms," Computers, Materials & Continua, vol. 67, no.3, pp. 3293–3310, 2021.
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