Designing Genetic Algorithms to Solve GIS Problems

2001 ◽  
Author(s):  
Dirk Thierens ◽  
Mark De Berg
Keyword(s):  
Genetic Algorithms ◽  
Building Block ◽  
Domain Knowledge ◽  
Geographical Information Systems ◽  
Optimal Solution ◽  
Building Blocks ◽  
Geographical Information ◽  
Specific Class ◽  
Map Labeling ◽  
Linkage Learning

What makes a problem hard for a genetic algorithm (GA)? How does one need to design a GA to solve a problem satisfactorily? How does the designer include domain knowledge in the GA? When is a GA suitable to use for solving a problem? These are all legitimate questions. This chapter will offer a view on genetic algorithms that stresses the role of the so-called linkage. Linkage relates to the fact that between the variables of the solution dependencies exist that cause a need to treat those variables as one “block,” since the best setting of each individual variable can only be determined by looking at the other variables as well. The genes that represent these variables will then have to be transferred together. When these genes are set to their optimal values, they constitute a building block. Building blocks will be transferred as a whole during recombination and the building blocks of all the genes make up the optimal solution. As will become apparent, knowing the linkage of a building block is a big advantage and will allow one to design efficient GAs. Sadly, in the majority of problems, the linkage is unknown. This observation has given rise to a lot of development in linkage learning algorithms (for an example, see Kargupta 1996). However, there is a specific class of problems that allows for relatively easy determination of linkage: spatial problems. This is because in these problems, the linkage is geometrically defined. We will focus in this chapter on certain hard problems that arise in the context of geographical information systems and for which the linkage can be easily found. Specifically, we will fully detail the design of a GA for the problem of map labeling, which is an important problem in automated cartography. The map labeling problem for point features is to find a placement for the labels of a set of points such that the number of labels that do not intersect other labels is maximized.

scholarly journals Street context of various demographic groups in their daily mobility

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Ariel Salgado ◽  
Weixin Li ◽  
Fahad Alhasoun ◽  
Inés Caridi ◽  
Marta Gonzalez
Keyword(s):  
Geographical Information Systems ◽  
Building Blocks ◽  
Geographical Information ◽  
Unified Framework ◽  
Demographic Groups ◽  
Daily Mobility ◽  
Urban Science ◽  
Science Framework ◽  
The City ◽  
Racial Ethnic

AbstractWe present an urban science framework to characterize phone users’ exposure to different street context types based on network science, geographical information systems (GIS), daily individual trajectories, and street imagery. We consider street context as the inferred usage of the street, based on its buildings and construction, categorized in nine possible labels. The labels define whether the street is residential, commercial or downtown, throughway or not, and other special categories. We apply the analysis to the City of Boston, considering daily trajectories synthetically generated with a model based on call detail records (CDR) and images from Google Street View. Images are categorized both manually and using artificial intelligence (AI). We focus on the city’s four main racial/ethnic demographic groups (White, Black, Hispanic and Asian), aiming to characterize the differences in what these groups of people see during their daily activities. Based on daily trajectories, we reconstruct most common paths over the street network. We use street demand (number of times a street is included in a trajectory) to detect each group’s most relevant streets and regions. Based on their street demand, we measure the street context distribution for each group. The inclusion of images allows us to quantitatively measure the prevalence of each context and points to qualitative differences on where that context takes place. Other AI methodologies can further exploit these differences. This approach presents the building blocks to further studies that relate mobile devices’ dynamic records with the differences in urban exposure by demographic groups. The addition of AI-based image analysis to street demand can power up the capabilities of urban planning methodologies, compare multiple cities under a unified framework, and reduce the crudeness of GIS-only mobility analysis. Shortening the gap between big data-driven analysis and traditional human classification analysis can help build smarter and more equal cities while reducing the efforts necessary to study a city’s characteristics.

Intelligence from Space

2011 ◽  
pp. 194-226
Author(s):  
Paul Hendriks
Keyword(s):  
Decision Making ◽  
Information Systems ◽  
Spatial Analysis ◽  
Geographical Information Systems ◽  
Geographical Information ◽  
Specific Class ◽  
Step Procedure ◽  
Decision Making Processes ◽  
Projected Position ◽  
Spatial Element

The spatial element, which is omnipresent in data and information relevant to organizations, is much underused in the decision-making processes within organizations. This applies also to decision-making within the domain of Competitive Intelligence. The chapter explores how the CI function may benefit from developing a spatial perspective on its domain and how building, exploring and using this perspective may be supported by a specific class of information systems designed to handle the spatial element in data: Geographical Information Systems (GIS). The chapter argues that the key element for linking GIS to CI involves the identification of situations in which spatial analysis may support organizational decision-making within the CI domain. It presents a three-step procedure for identifying how CI may recognize spatial decision problems that are useful to boost the operation of the CI function. The first step concerns identifying relevant spatial variables, for instance by analyzing economic, demographic or political trends as to their spatial implications. The second step involves using GIS for positioning the organization with respect to the identified variables (present and projected position). The third step amounts to drawing strategic conclusions from Step 2 by assessing how the competition in relationship with the own organization would be positioned along the identified spatial analysis lines.

scholarly journals Schemata Evolution and Building Blocks

1999 ◽  
Vol 7 (2) ◽  
pp. 109-124 ◽  
Author(s):  
Chris Stephens ◽  
Henri Waelbroeck
Keyword(s):  
Genetic Algorithms ◽  
Evolution Equation ◽  
Building Block ◽  
Building Blocks ◽  
Effective Fitness ◽  
Over Time ◽  
Low Order

In the light of a recently derived evolution equation for genetic algorithms we consider the schema theorem and the building block hypothesis. We derive a schema theorem based on the concept of effective fitness showing that schemata of higher than average effective fitness receive an exponentially increasing number of trials over time. The equation makes manifest the content of the building block hypothesis showing how fit schemata are constructed from fit sub-schemata. However, we show that, generically, there is no preference for short, low-order schemata. In the case where schema reconstruction is favored over schema destruction, large schemata tend to be favored. As a corollary of the evolution equation we prove Geiringer's theorem.

scholarly journals DECISION SUPPORT SYSTEM FOR SOLVING THE STREET ROUTING PROBLEM

Transport ◽  
2008 ◽  
Vol 23 (3) ◽  
pp. 230-235 ◽  
Author(s):  
Peter Matis
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Geographical Information Systems ◽  
Support Systems ◽  
Optimal Solution ◽  
Real Data ◽  
Geographical Information ◽  
Routing Problem ◽  
Time Required ◽  
Number Of Customers

Servicing a large number of customers in a city zone is often a considerable part of many logistics chains. The capacity of one delivery vehicle is limited, but, at the same time, it usually serves plenty of customers. This problem is often called a Street Routing Problem (SRP). Key differences between Vehicle Routing Problem (VRP) and SRP are presented here. The main problem of SRP is that when the number of customers is huge, the number of delivery path combinations becomes enormous. As the experimental results show in the case of SRP the error on the length of delivery routes based on an expert's judgment when compared to the optimal solution is in the range of 10–25%. As presented in the paper, only using decision support systems such as Geographical Information Systems (GIS) makes possible to effectively manage SRP. Besides classical measurements used in VRP, such as total length of routes or time required for delivery in each route, other measurements, mostly qualitative ones, are presented. All of these are named as visual attractiveness. This paper discusses possible relationships between quantitative and qualitative measurements that give a promise for finding better solutions of SRP. Several new types of heuristics for solving SRP are evaluated and afterward compared using the real data. One of the key properties of GIS to use routing software is its flexible interactive and user‐friendly environment. Routing software can find a good solution and explore the possibilities while an expert later can change the calculated routes to explore other possibilities based on the expert's judgment. This paper presents a practical use of new heuristics with the ArcView and solution of address mail for several cities in Slovakia served by Slovak Post ltd. Other Decision Support Systems that solve SRP are presented as TRANSCAD developed by Caliper Corporation or GeoRoute promoted by Canadian Post and GIRO.

Scalability Problems of Simple Genetic Algorithms

1999 ◽  
Vol 7 (4) ◽  
pp. 331-352 ◽  
Author(s):  
Dirk Thierens
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Evolutionary Computation ◽  
Building Block ◽  
Building Blocks ◽  
Research Topic ◽  
Problem Size ◽  
Clear Insight ◽  
Simple Genetic Algorithms ◽  
Algorithmic Design

Scalable evolutionary computation has. become an intensively studied research topic in recent years. The issue of scalability is predominant in any field of algorithmic design, but it became particularly relevant for the design of competent genetic algorithms once the scalability problems of simple genetic algorithms were understood. Here we present some of the work that has aided in getting a clear insight in the scalability problems of simple genetic algorithms. Particularly, we discuss the important issue of building block mixing. We show how the need for mixing places a boundary in the GA parameter space that, together with the boundary from the schema theorem, delimits the region where the GA converges reliably to the optimum in problems of bounded difficulty. This region shrinks rapidly with increasing problem size unless the building blocks are tightly linked in the problem coding structure. In addition, we look at how straightforward extensions of the simple genetic algorithm—namely elitism, niching, and restricted mating are not significantly improving the scalability problems.

A Comparison of the Fixed and Floating Building Block Representation in the Genetic Algorithm

1996 ◽  
Vol 4 (2) ◽  
pp. 169-193 ◽  
Author(s):  
Annie S. Wu ◽  
Robert K. Lindsay
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Building Block ◽  
Deoxyribonucleic Acid ◽  
Evolutionary Process ◽  
Building Blocks ◽  
Problem Representation ◽  
Parallel Search ◽  
Diverse Population ◽  
Significant Difference

This article compares the traditional, fixed problem representation style of a genetic algorithm (GA) with a new floating representation in which the building blocks of a problem are not fixed at specific locations on the individuals of the population. In addition, the effects of noncoding segments on both of these representations is studied. Noncoding segments are a computational model of noncoding deoxyribonucleic acid, and floating building blocks mimic the location independence of genes. The fact that these structures are prevalent in natural genetic systems suggests that they may provide some advantages to the evolutionary process. Our results show that there is a significant difference in how GAs solve a problem in the fixed and floating representations. Genetic algorithms are able to maintain a more diverse population with the floating representation. The combination of noncoding segments and floating building blocks appears to encourage a GA to take advantage of its parallel search and recombination abilities.

scholarly journals Risk based urban watershed management under conflicting objectives

2001 ◽  
Vol 43 (5) ◽  
pp. 69-78 ◽  
Author(s):  
V. Novotny ◽  
D. Clark ◽  
R. J. Griffin ◽  
D. Booth
Keyword(s):  
Geographical Information Systems ◽  
Flood Control ◽  
Optimal Solution ◽  
Geographical Information ◽  
Urban Stream ◽  
Urban Flood ◽  
Conflicting Objectives ◽  
Gis Environment ◽  
Ecological Preservation ◽  
Ecologically Sustainable

Ecological impairment and flooding caused by urbanization can be expressed numerically by calculating the risks throughout the watershed (floodplain) and along the main stems of the streams. The risks can be evaluated in terms of the present and/or future. This article describes the methodologies for ascertaining the risks in the Geographical Information Systems (GIS) environment. The objectives of urban flood controls and ecological preservation/restoration of urban waters are often conflicting and, in the past, the sole emphasis on flood control led to destruction of habitat and deterioration of water quality. An optimal solution to these two problems may be achieved by linking the risks to the concepts of risk communication, risk perception, and public willingness to pay for projects leading to ecological restoration and ecologically sustainable flood control. This method is appropriate because, in each case, public funds are used and the projects require approval and backing of policy makers and stakeholders. This article briefly describes a research project that attempts to resolve the conflict between the flood protection and stream ecological preservation and restoration and suggests alternative ways of expressing benefits of urban stream flood control and restoration projects.

scholarly journals How Crossover Speeds up Building Block Assembly in Genetic Algorithms

2017 ◽  
Vol 25 (2) ◽  
pp. 237-274 ◽  
Author(s):  
Dirk Sudholt
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Evolutionary Algorithms ◽  
Building Block ◽  
Broad Class ◽  
Statistical Tests ◽  
Building Blocks ◽  
Fundamental Question ◽  
Royal Road ◽  
Crossover Experiments

We reinvestigate a fundamental question: How effective is crossover in genetic algorithms in combining building blocks of good solutions? Although this has been discussed controversially for decades, we are still lacking a rigorous and intuitive answer. We provide such answers for royal road functions and OneMax, where every bit is a building block. For the latter, we show that using crossover makes every ([Formula: see text]+[Formula: see text]) genetic algorithm at least twice as fast as the fastest evolutionary algorithm using only standard bit mutation, up to small-order terms and for moderate [Formula: see text] and [Formula: see text]. Crossover is beneficial because it can capitalize on mutations that have both beneficial and disruptive effects on building blocks: crossover is able to repair the disruptive effects of mutation in later generations. Compared to mutation-based evolutionary algorithms, this makes multibit mutations more useful. Introducing crossover changes the optimal mutation rate on OneMax from [Formula: see text] to [Formula: see text]. This holds both for uniform crossover and k-point crossover. Experiments and statistical tests confirm that our findings apply to a broad class of building block functions.

Assessment of land-use change in coastal areas through geographical information systems

2020 ◽  
Vol 13 (10) ◽  
Author(s):  
Verónica Lango-Reynoso ◽  
Karla Teresa González-Figueroa ◽  
Fabiola Lango-Reynoso ◽  
María del Refugio Castañeda-Chávez ◽  
Jesús Montoya-Mendoza
Keyword(s):  
Land Use ◽  
Land Use Change ◽  
Geographical Information Systems ◽  
Design Methodology ◽  
Scientific Information ◽  
Coastal Ecosystems ◽  
Coastal Areas ◽  
Geographical Information ◽  
Coastal Zones ◽  
Computer Based

Objective: This article describes and analyzes the main concepts of coastal ecosystems, these as a result of research concerning land-use change assessments in coastal areas. Design/Methodology/Approach: Scientific articles were searched using keywords in English and Spanish. Articles regarding land-use change assessment in coastal areas were selected, discarding those that although being on coastal zones and geographic and soil identification did not use Geographic Information System (GIS). Results: A GIS is a computer-based tool for evaluating the land-use change in coastal areas by quantifying variations. It is analyzed through GIS and its contributions; highlighting its importance and constant monitoring. Limitations of the study/Implications: This research analyzes national and international scientific information, published from 2007 to 2019, regarding the land-use change in coastal areas quantified with the digital GIS tool. Findings/Conclusions: GIS are useful tools in the identification and quantitative evaluation of changes in land-use in coastal ecosystems; which require constant evaluation due to their high dynamism.

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