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_id	ddss9474
id	ddss9474
authors	Pereira, A.G., Munda, G. and Paruccini, M.
year	1994
title	Generating Alternatives For Siting Using Genetic Algorithms and Multiple Criteria Decision Techniques
source	Second Design and Decision Support Systems in Architecture & Urban Planning (Vaals, the Netherlands), August 15-19, 1994
summary	It is believed that a fundamental step in the structuring of a siting problem is generating alternati-ves. This task should occur at the beginning of a process for facility location, giving a preliminary insight into the feasibility of the project in the area of concern by identifying a manageable number of feasible alternatives for careful review and consideration. The purpose of this paper is to present a methodology aimed at generating alternatives for siting of facilities taking into account a number of criteria. These criteria comprise environmental, economical and the action's inherent technical aspects. The search is carried out by applying genetic algorithms (GA's) which are natural phenomena based algorithms for optimization and random search procedures. According to the GA's terminology, a fitness function measures the worth of each candidate alternative codified into a chromosome. It was thought that the merging of aspects of multiple criteria theory and genetic algorithms is essential for the problem of generating alternatives in location problems. The aim of this integration is the improvement of the theoretical principles upon which the fitness function is based, leading to the construction of a robust set of alternatives. The paper describes the integration of both multiple criteria theory and GA's and discusses the results.
series	DDSS
email
last changed	2003/08/07 16:36

_id	ddss9487
id	ddss9487
authors	Snijder, H.P.S.
year	1994
title	The Use of Genetic Algorithms in Spatial Optimisation Problems
source	Second Design and Decision Support Systems in Architecture & Urban Planning (Vaals, the Netherlands), August 15-19, 1994
summary	The manipulation of a set of associative data usually involves the search of a huge search-space (e.g. a set of 20 elements can be ordered in 20! ways, which is approximately equal to 2.4e+ 18). Rooms on a floor can be considered as a set of associative data. Optimising such a set according to some criterion (for example, minimising the distance between the related elements) can therefore be a daunting task. In order to assist in this task, a program (called ROP) has been developed, which graphically represents the relations in a matrix. The points in this matrix can be moved manually, thereby transforming the search process into a visual task. However, a considerable amount of skill remains required. In order to further alleviate the user in this task, ROP has been augmented with a Genetic Algorithm. A genetic algorithm is ideally suited to deal with very large search-spaces, and proved to be a valuable addition to ROP. In addition to employing the genetic algorithm for finding the optimal ordering, it can also be made to suggest several different orderings with approximately equal fitness, thereby providing elementary creativity support. The combination of ROP with a genetic algorithm provides a generic tool for the manipulation of all multivariate or associative data sets; in- as well as outside the design realm.
series	DDSS
last changed	2003/08/07 16:36

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