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W.M. Jenkins, "An Enhanced Genetic Algorithm for Structural Design Optimization", in B.H.V. Topping, A.I. Khan, (Editors), "Neural Networks & Combinatorial Optimization in Civil & Structural Engineering", Civil-Comp Press, Edinburgh, UK, pp 109-126, 1993. doi:10.4203/ccp.16.6.3

Abstract

The genetic algorithm is proving to be a useful tool in optimizing engineering design. In particular the algorithm is well suited to structural design, A population of individual designs, randomly generated, is changed generation-by-generation applying the principle of survival of the fittest. Engineering design is usually very extensively constrained so the fitness of a design is assessed using an objective function in which design constraint violations are penalized. A structured record of progress of the algorithm is used b adapt the control parameters and also forms the basis of heuristics leading to combinatorial space reduction for monotonic and non-monotonic data.

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Front Page Browse CCP CSETS CTR IJRT Other Authors Search Purchase Guide FAQ Contact us	Civil-Comp Proceedings ISSN 1759-3433 CCP: 16 NEURAL NETWORKS & COMBINATORIAL OPTIMIZATION IN CIVIL & STRUCTURAL ENGINEERING Edited by: B.H.V. Topping and A.I. Khan Paper VI.3 An Enhanced Genetic Algorithm for Structural Design Optimization W.M. Jenkins Department of Civil Engineering, University of Leeds, Leeds, England doi:10.4203/ccp.16.6.3 purchase the full-text of this paper Full Bibliographic Reference for this paper W.M. Jenkins, "An Enhanced Genetic Algorithm for Structural Design Optimization", in B.H.V. Topping, A.I. Khan, (Editors), "Neural Networks & Combinatorial Optimization in Civil & Structural Engineering", Civil-Comp Press, Edinburgh, UK, pp 109-126, 1993. doi:10.4203/ccp.16.6.3 Abstract The genetic algorithm is proving to be a useful tool in optimizing engineering design. In particular the algorithm is well suited to structural design, A population of individual designs, randomly generated, is changed generation-by-generation applying the principle of survival of the fittest. Engineering design is usually very extensively constrained so the fitness of a design is assessed using an objective function in which design constraint violations are penalized. A structured record of progress of the algorithm is used b adapt the control parameters and also forms the basis of heuristics leading to combinatorial space reduction for monotonic and non-monotonic data. purchase the full-text of this paper (price £20) go to the previous paper go to the next paper return to the table of contents return to the book description purchase this book (price £34 +P&P)
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