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Geographical optimization using evol...

Xiao, Ningchuan.

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Geographical optimization using evolutionary algorithms.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Geographical optimization using evolutionary algorithms./
作者:	Xiao, Ningchuan.
面頁冊數:	119 p.
附註:	Source: Dissertation Abstracts International, Volume: 64-07, Section: A, page: 2609.
Contained By:	Dissertation Abstracts International64-07A.
標題:	Geography. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3097595

Geographical optimization using evolutionary algorithms.
Xiao, Ningchuan.

Geographical optimization using evolutionary algorithms. - 119 p.

Source: Dissertation Abstracts International, Volume: 64-07, Section: A, page: 2609.

Thesis (Ph.D.)--The University of Iowa, 2003.

During the last two decades, evolutionary algorithms (EAs) have been applied to a wide range of optimization and decision-making problems. Work on EAs for geographical analysis, however, has been conducted in a problem-specific manner, which prevents an EA designed for one type of problem to be used on others. The purpose of this dissertation is to develop a framework that unifies the design and implementation of EAs for different types of geographical optimization problems. The key element in this framework is a graph representation that can be used to formally define the spatial structure of a broad range of geographical problems. Based on this representation, spatial constraints (e.g., contiguity and adjacency) of optimization problems can be effectively maintained, and general principles of designing evolutionary algorithms for geographical optimization are identified. The framework is applied to two types of problems that are often encountered in spatial analysis: site search and redistricting problems. The results suggest that the EA approach designed using this framework was successful in finding good (optimal or near-optimal) solutions to the test problems.Subjects--Topical Terms:

524010
Geography.

Geographical optimization using evolutionary algorithms.
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500 $a Source: Dissertation Abstracts International, Volume: 64-07, Section: A, page: 2609.
500 $a Supervisor: Marc P. Armstrong.
502 $a Thesis (Ph.D.)--The University of Iowa, 2003.
520 $a During the last two decades, evolutionary algorithms (EAs) have been applied to a wide range of optimization and decision-making problems. Work on EAs for geographical analysis, however, has been conducted in a problem-specific manner, which prevents an EA designed for one type of problem to be used on others. The purpose of this dissertation is to develop a framework that unifies the design and implementation of EAs for different types of geographical optimization problems. The key element in this framework is a graph representation that can be used to formally define the spatial structure of a broad range of geographical problems. Based on this representation, spatial constraints (e.g., contiguity and adjacency) of optimization problems can be effectively maintained, and general principles of designing evolutionary algorithms for geographical optimization are identified. The framework is applied to two types of problems that are often encountered in spatial analysis: site search and redistricting problems. The results suggest that the EA approach designed using this framework was successful in finding good (optimal or near-optimal) solutions to the test problems.
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650 4 $a Geography. $3 524010
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710 2 0 $a The University of Iowa. $3 1017439
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