ABSTRACT
Within the parallel genetic algorithm framework, there currently exists a growing dichotomy between coarse-grain and fine-grain parallel architectures. This chapter attempts to characterize the need for fine-grain parallelism, and to introduce and compare three models of fine-grain parallel genetic algorithms (GAs). The performance of the three models is examined on seventeen test problems and is compared to the performance of a coarse-grain parallel GA. Preliminary results indicate that the massive distribution of the fine-grain parallel GA and the modified population topology yield improvements in speed and in the number of evaluations required to find global optima.
