A genetic approach to standard cell placement using meta-genetic parameter optimization

representation of the physical layout. The algor i thm works on a set o f configurations constituting a constant size population. The transformations are performed through crossover operators that generate a new configuration assimilating the characteristics o f a pair o f configurations existing in the current population (similar to biological reproduction). Muta t ion and inversion operators are also used to increase the diversity o f the population, and avoid premature convergence at local optima. Due to the simultaneous optimization of a large populat ion of configurations, there i s a logical concurrency in the search of the solution space which makes the genetic algor i thm an extremely efficient optimizer. Three efficient crossover techniques have been compared, and the algor i thm parameters, namely mutat ion rate, crossover rate, and inversion rate have been optimized fo r the cell placement problem by using a meta-genetic process. The resulting algor i thm was tested against T i m b e r w o l f 3.3 on five industr ia l circuits consisting of 100-800 cells. The results indicate that a placement comparable in quality can be obtained in about the same execution time as T imberwo l f , but the genetic algor i thm needs to explore 20-50 times less configurations compared to T imberwo l f , which illustrates the efficiency o f the search process.