A SAND approach based on cellular computation models for analysis and optimization [Conference Paper]

Abstract

Genetic algorithms have received considerable recent attention in problems of design optimization. The mechanics of population-based search in GA's is highly amenable to implementation on parallel computers. The present paper describes a fine-grained model of parallel GA implementation that derives from a cellular-automata-like computation. The central idea behind the cellular genetic algorithm (CGA) approach is to treat the GA population as being distributed over a 2-D grid of cells, with each member of the population occupying a particular cell and defining the state of that cell. Evolution of the cell state is tantamount to updating the design information contained in a cell site, and as in cellular automata computations, takes place on the basis of local interaction with neighboring cells. A special focus of the paper is in the use of cellular automata (CA) based models for structural analysis in conjunction with the CGA approach to optimization. In such an approach, the analysis and optimization are evolved simultaneously in a unified cellular computational framework. The paper describes the implementation of this approach and examines its efficiency in the context of representative structural optimization problems. Copyright © 2004 by Prabhat Hajela.