CumInCAD is a Cumulative Index about publications in Computer Aided Architectural Design
supported by the sibling associations ACADIA, CAADRIA, eCAADe, SIGraDi, ASCAAD and CAAD futures

id acadia13_227
authors von Buelow, Peter
year 2013
title Techniques for More Productive Genetic Design: Exploration With GAs Using Non-Destructive Dynamic Populations
source ACADIA 13: Adaptive Architecture [Proceedings of the 33rd Annual Conference of the Association for Computer Aided Design in Architecture (ACADIA) ISBN 978-1-926724-22-5] Cambridge 24-26 October, 2013), pp. 227-234
summary The products of generative design are ever more commonly explored and refined through evolutionary search techniques. Genetic algorithms (GAs) belong to this class of stochastic procedures, and are particularly well-suited to the way designers investigate a problem. GAs search by mixing and matching different parts of a solution, represented as parametric variables, to find new solutions that outperform their predecessors. Generally the method proceeds through generations of populations in which the better solutions out-survive their less desirable siblings. Inherent to this approach, however, is the fact that all but the select solutions perish. This paper discusses a non-destructive GA that uses dynamic populations drawn from a bottomless pool of solutions to find the most productive breeding pairs. In a typical GA the survival or destruction of a solution depends on a well-defined fitness function. By not enforcing the destruction of less fit individuals, the possibility is held open to modify the fitness function at any time, and allow different parts of the solution space to be explored. This ability is ideal for more complex multi-objective problems that are not easily described by a single fitness function. Generally, design presents just such a problem.
keywords tools and interfaces, design exploration, genetic algorithm, multi-objective optimization
series ACADIA
type Normal Paper
email pvbuelow@umich.edu
full text file.pdf (1,782,305 bytes)
references Content-type: text/plain
details citation check to select
100%; open Beasley, David (1997) Find in CUMINCAD Possible applications of evolutionary computation , Bäck, Thomas; Fogel, David and Michalewicz, Zbigniew. (eds.). Handbook of Evolutionary Computation. Oxford and New York: Oxford University Press
100%; open Deb, Kalyanmoy (1997) Find in CUMINCAD Selection - Introduction , Bäck, Thomas; Fogel, David and Michalewicz, Zbigniew. (eds.). Handbook of Evolutionary Computation. Oxford and New York: Oxford University Press
100%; open Eshelman, Larry (1991) Find in CUMINCAD The CHC Adaptive Search Algorithm: How to Have Safe Search When Engaging in Nontraditional Genetic Recombination , Foundations of Genetic Algorithms. San Mateo, California: Morgan Kaufmann Publ
100%; open Goldberg, David (8008) Find in CUMINCAD The Design of Innovation : Lessons from and for Competent Genetic Algorithms , Boston: Kluwer Academic Publishers
100%; open Goldberg, David (1989) Find in CUMINCAD Genetic algorithms in search, optimization, and machine learning , Reading, Mass: Addison-Wesley Publishing Company, Inc
100%; open Holland, John (1975) Find in CUMINCAD Adaptation in Natural and Artificial Systems , Ann Arbor, Mich.: The University of Michigan Press

last changed 2014/01/11 08:13
HOMELOGIN (you are user _anon_331513 from group guest) Works Powered by SciX Open Publishing Services 1.002