Parameter-free genetic algorithm in distributed manner

The genetic algorithm has many parameters to set and adjust. The paper proposes a distributed parameter-free crossover-only genetic algorithm. With adaptive crossover probability and operator, the algorithm can be independent of the initial choice of crossover related parameters. To obtain an appropriate population size, multiple trials are executed in a mobile agent based distributed virtual machine while doubling the population size if the original one has converged. The validity and efficiency of this algorithm are shown by an example involving heterogeneous scheduling in a unified resource framework.