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Paper I.5

City Planning using a Multiobjective Genetic Algorithm and an Interactive Pareto Set Scanner

R. Balling, K. Day and J. Taber

Department of Civil and Structural Engineering, Brigham Young University, Provo, Utah, United States of America

doi:10.4203/ccp.60.1.5

R. Balling, K. Day, J. Taber, "City Planning using a Multiobjective Genetic Algorithm and an Interactive Pareto Set Scanner", in B.H.V. Topping, B. Kumar, (Editors), "Optimization and Control in Civil and Structural Engineering", Civil-Comp Press, Edinburgh, UK, pp 35-39, 1999. doi:10.4203/ccp.60.1.5

Abstract

A genetic algorithm was used to search for optimal future land use and transportation plans for a high-growth city. Millions of plans were considered. A constraint was imposed to insure adequate housing for future residents. Objectives included the minimization of traffic congestion, the minimization of costs, and the minimization of change from the status quo. The genetic algorithm provided planners and decision-makers with a set of optimal plans known as the Pareto set. An interactive computer tool was developed to assist decision-makers in scanning the information in the Pareto set.

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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: 60 OPTIMIZATION AND CONTROL IN CIVIL AND STRUCTURAL ENGINEERING Edited by: B.H.V. Topping and B. Kumar Paper I.5 City Planning using a Multiobjective Genetic Algorithm and an Interactive Pareto Set Scanner R. Balling, K. Day and J. Taber Department of Civil and Structural Engineering, Brigham Young University, Provo, Utah, United States of America doi:10.4203/ccp.60.1.5 purchase the full-text of this paper Full Bibliographic Reference for this paper R. Balling, K. Day, J. Taber, "City Planning using a Multiobjective Genetic Algorithm and an Interactive Pareto Set Scanner", in B.H.V. Topping, B. Kumar, (Editors), "Optimization and Control in Civil and Structural Engineering", Civil-Comp Press, Edinburgh, UK, pp 35-39, 1999. doi:10.4203/ccp.60.1.5 Abstract A genetic algorithm was used to search for optimal future land use and transportation plans for a high-growth city. Millions of plans were considered. A constraint was imposed to insure adequate housing for future residents. Objectives included the minimization of traffic congestion, the minimization of costs, and the minimization of change from the status quo. The genetic algorithm provided planners and decision-makers with a set of optimal plans known as the Pareto set. An interactive computer tool was developed to assist decision-makers in scanning the information in the Pareto set. 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 £66 +P&P)
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