The Evolution of Genetic Algorithms: Towards Massive Parallelism
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.
BibTeX
@conference{Baluja-1993-15930,author = {Shumeet Baluja},
title = {The Evolution of Genetic Algorithms: Towards Massive Parallelism},
booktitle = {Proceedings of (ICML) International Conference on Machine Learning},
year = {1993},
month = {June},
editor = {P.E. Utgoff},
pages = {1 - 8},
publisher = {Morgan Kaufmann Publishers},
}