Abstract
One of the issues in creating any search technique is balancing the need for diverse exploration with the desire for efficient focusing. This paper explores a genetic algorithm (GA) architecture which is more resilient to local optima than other recently introduced GA models, and which provides the ability to focus search quickly. The GA uses a fine-grain parallel architecture to simulate evolution more closely than previous models. In order to motivate the need for fine-grain parallelism, this paper will provide an overview of the two preceding phases of development: the traditional genetic algorithm, and the coarse-grain parallel GA. A test set of 15 problems is used to compare the effectiveness of a fine-grain parallel GA with that of a coarse-grain parallel GA.
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