Abstract-Gravitational Search Algorithms (GSA) are heuristic optimization evolutionary algorithms based on Newton's law of universal gravitation and mass interactions. GSAs are among the most recently introduced techniques that are not yet heavily explored. An early work of the authors has successfully adapted this technique to the cell placement problem, and shown its efficiency in producing high quality solutions in reasonable time. We extend this work by fine tuning the algorithm parameters and transition functions towards better balance between exploration and exploitation. To assess its performance and robustness, we compare it with that of Genetic Algorithms (GA), using the standard cell placement problem as benchmark to evaluate the solution quality, and a set of artificial instances to evaluate the capability and possibility of finding an optimal solution. Experimental results show that the proposed approach is competitive in terms of success rate or likelihood of optimality and solution quality. And despite that it is computationally more expensive due to its hefty mathematical evaluations, it is more fruitful on the long run.
Cell placement is a phase in the chip design process, in which cells representing well-defined functions are assigned physical locations. Cell placement is an NPcomplete problem, for which we intend to devise an adaptive genetic algorithm. Genetic algorithms have many parameters such as population size, mutation rate, crossover rate, and selection strategy, which are constants most ofthe time and need to be carefully setfor efficient implementation. However, adaptive approaches tend to vary one or more of those parameters as the process evolve. In this work, we propose a scheme to adjust the population size in a way that provides a balance between exploration and exploitation, hence result in a time-efficient implementation of genetic algorithms. We compare this scheme with three sizing schemes proposed in the literature.
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