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http://dx.doi.org/10.5392/JKCA.2021.21.07.164

A Study of Adapted Genetic Algorithm for Circuit Partitioning  

Song, Ho-Jeong (충북대학교 컴퓨터공학과)
Kim, Hyun-Gi (강동대학교 소방안전과)
Publication Information
The Journal of the Korea Contents Association / v.21, no.7, 2021 , pp. 164-170 More about this Journal
Abstract
In VLSI design, partitioning is a task of clustering objects into groups so that a given objective circuit is optimized. It is used at the layout level to find strongly connected components that can be placed together in order to minimize the layout area and propagation delay. The most popular algorithms for partitioning include the Kernighan-Lin algorithm, Fiduccia-Mattheyses heuristic and simulated annealing. In this paper, we propose a adapted genetic algorithm searching solution space for the circuit partitioning problem, and then compare it with simulated annealing and genetic algorithm by analyzing the results of implementation. As a result, it was found that an adaptive genetic algorithm approaches the optimal solution more effectively than the simulated annealing and genetic algorithm.
Keywords
Genetic Algorithm; Circuit Partition; Simulated Annealing; Adapted Genetic Algorithm;
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