Revisiting Genetic Algorithms for the FPGA Placement Problem

In this work, we present a genetic algorithm framework for the FPGA placement problem. This framework is constructed based on previous proposals in this domain. We implement this framework in an academic FPGA tool, and run a set of experiments that show that the fine grain genetic mutation approach, previously proposed, is not as good as an existing simulated annealing algorithm. This does not discount the use of genetic algorithms in this domain, and instead, provides motivation to explore other aspects of the problem to apply genetic algorithms to this