A study of permutation crossover operators on the traveling salesman problem
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
VLSI cell placement techniques
ACM Computing Surveys (CSUR)
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
AllelesLociand the Traveling Salesman Problem
Proceedings of the 1st International Conference on Genetic Algorithms
A genetic algorithm for macro cell placement
EURO-DAC '92 Proceedings of the conference on European design automation
Evolutionary algorithms for the physical design of VLSI circuits
Advances in evolutionary computing
A genetic algorithm for channel routing in vlsi circuits
Evolutionary Computation
An improved genetic algorithm for cell placement
ICIC'06 Proceedings of the 2006 international conference on Intelligent Computing - Volume Part I
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This paper describes the implementation of the Genetic Algorithm for Standard-cell Placement, GASP-1. As opposed to simulated annealing, which normally uses pairwise interchange for transforming the layout configuration, in a genetic algorithm, the crossover operator is used to combine two current configurations to generate a new configuration (similar to reproduction in living organisms). The traditional genetic crossover operator, as proposed by Holland, cannot be applied to the cell placement problem without modification, because it occasionally results in illegal placement. A great deal of effort has there fore been directed towards finding an efficient crossover operator for this problem domain. Three powerful crossover operators have been implemented, and their performance in reducing the interconnect length has been compared. The results of this comparison were conclusively in favor of Cycle crossover. Besides crossover, two other genetic operators --- mutation and inversion --- have been used to improve the efficiency of the search process. In order to benchmark the performance of GASP-1, the best possible compromise of the parameters was picked, and the algorithm was run to place five industrial circuits consisting of 100 to 800 cells. The results were very encouraging. The total number of configurations examined by GASP-1 was 19 to 50 times less than that for Timber Wolf 3.3 and the run time was marginally better. The percentage improvement in the wire length was better in three out of five circuits The overall conclusion from this research is that adaptive search based on the genetic algorithm yields results comparable to simulated annealing, both in the final result quality and computation time required.