Hybrid parallelization of a compact genetic algorithm

Genetic algorithms (GAs) are stochastic optimization heuristics in which searches in solution space are carried out by imitating the population genetics stated in Darwin's theory of evolution. We have focused this work on compact genetic algorithms (cGAs), which unlike standard GAs do not manage a population of solutions but only mimics its existence. We study several approaches that can be used to implement parallel cGAs in order to reduce the execution times and to improve the quality of the solutions reached by increasing population sizes. The parallelization models adopted to implement GAs can be classified as: centralized, global, fine grained and coarse grained. For a cGA, only the two first models can be applied. Our approach consists of a hybrid model which combines both centralized and global implementations. The cGA incorporates a local search method and has been applied for solving a graph-partitioning problem for solving the Multi-FPGA systems partitioning and placement.