http://de.evo-art.org/index.php?action=history&feed=atom&title=Improving_Pareto_optimal_designs_using_genetic_algorithmsImproving Pareto optimal designs using genetic algorithms - Versionsgeschichte2026-04-05T16:57:42ZVersionsgeschichte dieser Seite in de_evolutionary_art_orgMediaWiki 1.27.4http://de.evo-art.org/index.php?title=Improving_Pareto_optimal_designs_using_genetic_algorithms&diff=2108&oldid=prevGbachelier: Die Seite wurde neu angelegt: „== Reference == Gero, JS and Louis, S (1995): Improving Pareto optimal designs using genetic algorithms. Microcomputers in Civil Engineering 10(4): 241-249. …“2014-12-08T11:10:49Z<p>Die Seite wurde neu angelegt: „== Reference == Gero, JS and Louis, S (1995): Improving Pareto optimal designs using genetic algorithms. Microcomputers in Civil Engineering 10(4): 241-249. …“</p>
<p><b>Neue Seite</b></p><div>== Reference ==<br />
Gero, JS and Louis, S (1995): Improving Pareto optimal designs using genetic algorithms. Microcomputers in Civil Engineering 10(4): 241-249. <br />
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== DOI ==<br />
http://dx.doi.org/10.1111/j.1467-8667.1995.tb00286.x <br />
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== Abstract ==<br />
Pareto optimal designs are the best designs that can be produced for a given problem formulation for a given set of criteria when the criteria are not combined in any way. If the goal is to improve the performance in those criteria, then it is possible to manipulate the problem formulation to achieve an improvement. The approach adopted is to encode the formulation in a genetic algorithm and to allow the formulation to evolve in the direction of improving Pareto optimal designs. A set of rules (in the form of a shape grammar), the execution of which produces a design, is encoded as the genes in a genetic algorithm. However, the rule set is allowed to evolve, not just the order of execution of rules. We present an example demonstrating both the approach and its utility in improving Pareto optimal designs.<br />
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== Extended Abstract ==<br />
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== Bibtex == <br />
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== Used References ==<br />
1. Adeli, H. and Cheng, N. T. (1994). Integrated genetic algorithm for optimization of<br />
space structures, ASCE Journal of Aerospace Engineering 6(4): 315{328.<br />
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2. Gero, J. S. (1990). Design prototypes: a knowledge representation schema for design,<br />
AI Magazine 11(4): 26{36.<br />
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3. Goldberg, D. E. (1989). Genetic Algorithms in Search, Optimization, and Machine<br />
Learning, Addison-Wesley, Reading, Massachusetts.<br />
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4. Holland, J. H. (1975). Adaptation in Natural and Arti cial Systems, University of<br />
Michigan Press, Ann Arbour, Michigan.<br />
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5. Louis, S. J. and Rawlins, G. J. E. (1992). Syntactic analysis of crossover in ge-<br />
netic algorithms, in D. Whitley (ed.), Foundations of Genetic Algorithms{2, Morgan<br />
Kaufmann, San Mateo, CA, pp. 141{152.<br />
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6. Radford, A. D. and Gero, J. S. (1988). Design by Optimization in Architecture,<br />
Building and Construction, Van Nostrand Reinhold, New York.<br />
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7. Stiny, G. and Gips, J. (1978). Algorithm Aesthetics: Computer Models for Criticism<br />
and Design in the Arts, University of California Press, Berkeley and Los Angeles,<br />
California.<br />
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== Links ==<br />
=== Full Text === <br />
http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.36.4908&rep=rep1&type=pdf<br />
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[[intern file]]<br />
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=== Sonstige Links ===<br />
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.36.4908</div>Gbachelier