2010•Unpublished venueRequires access

A new genetic simulated annealing algorithm for hardware-software partitioning

Lanying Li, Yanbo Song, Ming Yu Gao

Open publisher page 8 citations

Abstract

To solve the hardware/software partitioning problem in embedded system, this paper proposed a new genetic simulated annealing algorithm (NGSA) which based on analysis of genetic algorithms and simulated annealing algorithm the main advantages and disadvantages. The genetic algorithm integrates the simulated annealing idea; niche technology is introduced to maintain population diversity; and the Metropolis criterion with the formation of new groups to improve the quality of group. Experimental results show that the algorithm has strong climbing ability and global search capability, and the fitness value is significantly improved than genetic algorithm and simulated annealing algorithm.

About this research paper

What this paper is about

To solve the hardware/software partitioning problem in embedded system, this paper proposed a new genetic simulated annealing algorithm (NGSA) which based on analysis of genetic algorithms and simulated annealing algorithm the main advantages and disadvantages. The genetic algorithm integrates the simulated annealing idea; niche technology is introduced to maintain population diversity; and the Metropolis criterion with the formation of new groups to improve the quality of group. Experimental results show that the algorithm has strong climbing ability and global search capability, and the fitness value is significantly improved than genetic algorithm and simulated annealing algorithm.

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OpenAlex reports 8 citations for this work. Citation counts describe recorded attention and do not establish research quality.

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Available abstract

To solve the hardware/software partitioning problem in embedded system, this paper proposed a new genetic simulated annealing algorithm (NGSA) which based on analysis of genetic algorithms and simulated annealing algorithm the main advantages and disadvantages. The genetic algorithm integrates the simulated annealing idea; niche technology is introduced to maintain population diversity; and the Metropolis criterion with the formation of new groups to improve the quality of group. Experimental results show that the algorithm has strong climbing ability and global search capability, and the fitness value is significantly improved than genetic algorithm and simulated annealing algorithm.

Key concepts: Simulated annealing, Hill climbing, Adaptive simulated annealing, Genetic algorithm, Computer science, Algorithm, Software, Population

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