AI SearchConsiderations on population size, crossover and mutation operators in genetic algorithm for VLSI floor plan design
Call Number: AIT Thesis no. CS-91-35 Material type:
TextSeries: Asian Institute of Technology. Thesis ; no. CS-91-35Publication details: Bangkok : Asian Institute of Technology, 1991Description: 69 pSubject(s): Online resources: Dissertation note: Thesis (M.Eng.) - Asian Institute of Technology, 1991 Summary: Floorplan design is an important stage in VLSI design. Designing a floorplan requires an arrangement of a given set of modules in a plane, to minimize the weighted sum of area and wirelength measures. This study appraises the use of genetic algorithm for solving floorplan design. Using it as an example, the effect of population size, mutation and selection technique on GA was studied and a comparative study of genetic operators was also carried out to find better crossover operators for VLSI floorplan design. The results obtained from above mentioned study were used to find optimal solution for VLSI floorplan design. Eventually, the effect of adjacency constraints on floorplan design was investigated. The result shows that Genetic Algorithm is effective for finding near optimal solution for VLSI floorplan design.
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22-AIT Thesis (Replacement)
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40-Archives
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Asian Institute of Technology Library Archives | AIT Thesis no. CS-91-35 (Browse shelf(Opens below)) | Available | 30050120353379 |
A thesis report submitted in partial fulfillment of the requirements for the Degree of Master of Engineering, School of Engineering and Technology
Thesis (M.Eng.) - Asian Institute of Technology, 1991
Floorplan design is an important stage in VLSI design. Designing a floorplan requires an arrangement of a given set of modules in a plane, to minimize the weighted sum of area and wirelength measures. This study appraises the use of genetic algorithm for solving floorplan design. Using it as an example, the effect of population size, mutation and selection technique on GA was studied and a comparative study of genetic operators was also carried out to find better crossover operators for VLSI floorplan design. The results obtained from above mentioned study were used to find optimal solution for VLSI floorplan design. Eventually, the effect of adjacency constraints on floorplan design was investigated. The result shows that Genetic Algorithm is effective for finding near optimal solution for VLSI floorplan design.
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