Genetic Algorithms, originally inspired by the nature, have come a long a way in the past decade to establish itself as a numerical optimization tool of choice. The simplicity of its structure, robustness of its operation, global view of its search technique, efficiency of its sorting though the unknown, all contribute to the excellence of genetic algorithm as an engineering design optimization tool. With the advent of modern parallel and distributed supercomputer systems, there is a renewed enthusiasm about fast genetic algorithms that efficiently solves very large numerical optimization problems utilizing these machines. The working of genetic algorithms is analyzed to extract parallelism in its functionality. The effects of various parallelization proposals and details of a parallel implementation are provided. Several representative problem solutions are presented to demonstrate the utility of the implementation. These indicate a strong future for parallel genetic algorithms for solving a large variety of scientific, engineering, and design problems.
['Mir M. Atiqullah']
Problem independent parallel genetic algorithm for design optimization