Regional Land Use and Transportation Planning with a Genetic Algorithm

2003 ◽  
Vol 1831 (1) ◽  
pp. 210-218 ◽  
Author(s):  
Richard Balling ◽  
Michael Lowry ◽  
Mitsuru Saito
Keyword(s):  
Genetic Algorithm ◽  
Land Use ◽  
Regional Planning ◽  
Traffic Congestion ◽  
Transportation Planning ◽  
Fitness Function ◽  
Search Space ◽  
Metropolitan Region ◽  
Search Spaces ◽  
Regional Land

A new approach to regional land use and transportation planning, which uses a genetic algorithm as an integrated optimization tool, is presented. The approach is illustrated by applying it to the Wasatch Front Metropolitan Region, which consists of four counties in the state of Utah. This genetic algorithm–-based approach was applied earlier to the twin cities of Provo and Orem in Utah, but here it is adapted to regional planning. Three issues make regional planning particularly difficult: ( a) individual cities have significant planning autonomy, ( b) the search space of possible plans is immense, and ( c) preferences between competing objectives vary among stakeholders. The approach used here addresses the first issue by the way the problem is formulated. The second issue is addressed with a genetic algorithm. Such algorithms are particularly well suited to problems with large search spaces. The third issue is addressed by using a multiobjective fitness function in the genetic algorithm. It was found that a genetic algorithm could produce a set of nondominated future land use scenarios and street plans for a region, from which regional planners can make a selection. Execution of the algorithm to produce 100 plans per generation for 100 generations took about 4 days with a high-end personal computer. Interesting trends for reducing change and traffic congestion were discovered.

Land Use and Transportation Planning for Twin Cities Using a Genetic Algorithm

2000 ◽  
Vol 1722 (1) ◽  
pp. 67-74 ◽  
Author(s):  
Richard J. Balling ◽  
John Taber ◽  
Kirsten Day ◽  
Scott Wilson
Keyword(s):  
Genetic Algorithm ◽  
Land Use ◽  
Transportation Planning ◽  
Fitness Function ◽  
High Growth ◽  
Pareto Set ◽  
Decision Makers ◽  
Twin Cities ◽  
New Approach ◽  
Future Plans

A new approach to future land use and transportation planning for high-growth cities is presented. The approach employs a genetic algorithm to efficiently search through hundreds of thousands of possible future plans. A new fitness function is developed to guide the genetic algorithm toward a Pareto set of plans for the multiple competing objectives that are involved. This set may be placed before decision makers. A Pareto set scanner also is described that assists decision makers in shopping through the Pareto set to select a plan. Some of the differences between simultaneous planning and separate planning of highly coupled twin cities also are examined.

Global Path Planning for Full-Area Coverage Robotic Systems by Employing an Active Genetic Algorithm

2011 ◽  
Vol 328-330 ◽  
pp. 1881-1886
Author(s):  
Cen Zeng ◽  
Qiang Zhang ◽  
Xiao Peng Wei
Keyword(s):  
Genetic Algorithm ◽  
Path Planning ◽  
High Performance ◽  
Search Space ◽  
Area Coverage ◽  
Robotic Systems ◽  
Genetic Operators ◽  
A Algorithm ◽  
Coverage Path Planning ◽  
Search Spaces

Genetic algorithm (GA), a kind of global and probabilistic optimization algorithms with high performance, have been paid broad attentions by researchers world wide and plentiful achievements have been made.This paper presents a algorithm to develop the path planning into a given search space using GA in the order of full-area coverage and the obstacle avoiding automatically. Specific genetic operators (such as selection, crossover, mutation) are introduced, and especially the handling of exceptional situations is described in detail. After that, an active genetic algorithm is introduced which allows to overcome the drawbacks of the earlier version of Full-area coverage path planning algorithms.The comparison between some of the well-known algorithms and genetic algorithm is demonstrated in this paper. our path-planning genetic algorithm yields the best performance on the flexibility and the coverage. This meets the needs of polygon obstacles. For full-area coverage path-planning, a genotype that is able to address the more complicated search spaces.

scholarly journals Constrained Novelty Search: A Study on Game Content Generation

2015 ◽  
Vol 23 (1) ◽  
pp. 101-129 ◽  
Author(s):  
Antonios Liapis ◽  
Georgios N. Yannakakis ◽  
Julian Togelius
Keyword(s):  
Genetic Algorithm ◽  
Search Space ◽  
Selection Method ◽  
Search Algorithms ◽  
Genetic Operators ◽  
Search Methods ◽  
Search Spaces ◽  
Novelty Search ◽  
Feasible Space ◽  
Content Generation

Novelty search is a recent algorithm geared toward exploring search spaces without regard to objectives. When the presence of constraints divides a search space into feasible space and infeasible space, interesting implications arise regarding how novelty search explores such spaces. This paper elaborates on the problem of constrained novelty search and proposes two novelty search algorithms which search within both the feasible and the infeasible space. Inspired by the FI-2pop genetic algorithm, both algorithms maintain and evolve two separate populations, one with feasible and one with infeasible individuals, while each population can use its own selection method. The proposed algorithms are applied to the problem of generating diverse but playable game levels, which is representative of the larger problem of procedural game content generation. Results show that the two-population constrained novelty search methods can create, under certain conditions, larger and more diverse sets of feasible game levels than current methods of novelty search, whether constrained or unconstrained. However, the best algorithm is contingent on the particularities of the search space and the genetic operators used. Additionally, the proposed enhancement of offspring boosting is shown to enhance performance in all cases of two-population novelty search.

scholarly journals Dynamic models of multicriteria to combine variables: the use of parametric model and genetic algorithm in the study of the World Heritage area recognized by UNESCO in Pampulha, Brazil

2019 ◽  
Vol 14 (1) ◽  
pp. 142-159
Author(s):  
Silvio Romero Fonseca Motta ◽  
Ana Clara Mourão Moura ◽  
Suellen Roquete Ribeiro
Keyword(s):  
Genetic Algorithm ◽  
Land Use ◽  
Land Use Change ◽  
Dynamic Models ◽  
Fitness Function ◽  
Land Use Changes ◽  
Multicriteria Analysis ◽  
World Heritage ◽  
Parametric Model ◽  
Belo Horizonte

The present paper surveys dynamic models of multicriteria to combine variables using parametric model and genetic algorithm as a method of changing the adequacy level of variables in a multicriteria analysis (MCA). The aim is to simulate if-then scenarios of territorial occupation of commerce, housing and green areas. The case study is a MCA for the buffer zone of the modern assembly of Niemeyer in Pampulha region, Belo Horizonte, Brazil, declared World Heritage by UNESCO. The parametric model was developed in Grasshopper software. The level of adequacy/score of the territorial units to characterize attractiveness and vulnerabilities to land use change was defined by "knowledge-driven" in the layers: Safety Risks, Fragility in Infrastructure, Bus Stop and Centralities due to Interaction Potential. The land use change simulation "if-then" was defined by "objective-driven", due the use of fitness-function in genetic algorithm, with the goal to achieve the best distribution of land use changes, in order to result in a more balanced use of the territory (commerce, housing or vegetation), but also considering attractiveness and vulnerabilities defined by the characteristics of the neighborhoods (centralities, transportation, safety and fragilities in infrastructure). The parametric model generates “if-then” simulation, calculating an index of suitability for each territorial unit and changing the land use according to the objective-driven to be achieved in fitness-function.

scholarly journals Half-Century Changes in LULC and Fire in Two Iberian Inner Mountain Areas

Fire ◽  
2019 ◽  
Vol 2 (3) ◽  
pp. 45
Author(s):  
Catarina Romão Sequeira ◽  
Francisco Rego ◽  
Cristina Montiel-Molina ◽  
Penelope Morgan
Keyword(s):  
Land Use ◽  
Regional Planning ◽  
Mountain Areas ◽  
Social Ecological ◽  
Mountain System ◽  
Land Use Policies ◽  
In Fire ◽  
The Relationship ◽  
Regional Land

Wildfires in the Iberian Peninsula were large and frequent in the second half of the 20th century. Land use and land cover (LULC) also changed greatly. Our aim was to understand the relationship between LULC and fire in the western and eastern ends of the Iberian Central Mountain System. We compared two case study landscapes, the Estrela massif and the Ayllón massif, which are biophysically similar but with different social-ecological contexts. In both, fires were in general more likely in shrublands and pastures than in forests. Shrublands replaced forests after fires. Contrasting LULC in the two massifs, particularly pastures, likely explained the differences in fire occurrence, and reflected different regional land use policies and history. Fire here is a social-ecological system, influenced by specific LULC and with implications from landscape to regional scales. Understanding how LULC changes interact with fire is powerful for improving landscape and regional planning.

scholarly journals Experimental Programming of Genetic Algorithms for the “Casse-Tête” Problem

2022 ◽  
Vol 13 (1) ◽  
pp. 0-0
Keyword(s):  
Genetic Algorithm ◽  
Fitness Function ◽  
Search Space ◽  
Rank Test ◽  
Mutation Operators ◽  
Signed Rank ◽  
Significant Difference ◽  
Chromosome Representation ◽  
Wilcoxon Signed Rank Test ◽  
Signed Rank Test

The Casse-tête board puzzle consists of an n×n grid covered with n^2 tokens. m<n^2 tokens are deleted from the grid so that each row and column of the grid contains an even number of remaining tokens. The size of the search space is exponential. This study used a genetic algorithm (GA) to design and implement solutions for the board puzzle. The chromosome representation is a matrix of binary permutations. Variants for two crossover operators and two mutation operators were presented. The study experimented with and compared four possible operator combinations. Additionally, it compared GA and simulated annealing (SA)-based solutions, finding a 100% success rate (SR) for both. However, the GA-based model was more effective in solving larger instances of the puzzle than the SA-based model. The GA-based model was found to be considerably more efficient than the SA-based model when measured by the number of fitness function evaluations (FEs). The Wilcoxon signed-rank test confirms a significant difference among FEs in the two models (p=0.038).

Hybrid Optimization Algorithm Using Enhanced Continuous Tabu Search and Genetic Algorithm for Parameter Estimation

2006 ◽  
Author(s):  
Barathram Ramkumar ◽  
Marco P. Schoen ◽  
Feng Lin ◽  
Brian G. Williams
Keyword(s):  
Genetic Algorithm ◽  
Parameter Estimation ◽  
Tabu Search ◽  
Search Space ◽  
The Novel ◽  
Search Spaces ◽  
Estimation Algorithms ◽  
Hybrid Optimization Algorithm ◽  
Estimation Problems ◽  
Promising Area

A new algorithm using Enhanced Continuous Tabu Search (ECTS) and genetic algorithm (GA) is proposed for parameter estimation problems. The proposed algorithm combines the respective strengths of ECTS and GA. The ECTS is a modified Tabu Search (TS), which has good search capabilities for large search spaces. In this work, the ECTS is used to define smaller search spaces, which are used in a second stage by a GA to find the respective local minima. The ECTS covers the global search space by using a TS concept called diversification and then selects the most promising regions in the search space. Once the promising areas in the search space are identified, the proposed algorithm employs another TS concept called intensification in order to search the promising area thoroughly. The proposed algorithm is tested with benchmark multimodal functions for which the global minimum is known. In addition, the novel algorithm is used for parameter estimation problems, where standard estimation algorithms encounter problems estimating the parameters in an un-biased fashion. The simulation results indicate the effectiveness of the proposed hybrid algorithm.

scholarly journals Variable Search Space Converging Genetic Algorithm for Solving System of Non-linear Equations

2020 ◽  
Vol 30 (1) ◽  
pp. 142-164 ◽  
Author(s):  
Venkatesh SS ◽  
Deepak Mishra
Keyword(s):  
Genetic Algorithm ◽  
Optimization Problems ◽  
Fitness Function ◽  
Linear Equations ◽  
Search Space ◽  
New Variant ◽  
Non Linear ◽  
Coarse Search ◽  
Non Linear Equations ◽  
Digit Length

Abstract This paper introduce a new variant of the Genetic Algorithm whichis developed to handle multivariable, multi-objective and very high search space optimization problems like the solving system of non-linear equations. It is an integer coded Genetic Algorithm with conventional cross over and mutation but with Inverse algorithm is varying its search space by varying its digit length on every cycle and it does a fine search followed by a coarse search. And its solution to the optimization problem will converge to precise value over the cycles. Every equation of the system is considered as a single minimization objective function. Multiple objectives are converted to a single fitness function by summing their absolute values. Some difficult test functions for optimization and applications are used to evaluate this algorithm. The results prove that this algorithm is capable to produce promising and precise results.

scholarly journals Practical Efficient Regional Land-Use Planning Using Constrained Multi-Objective Genetic Algorithm Optimization

2021 ◽  
Vol 10 (2) ◽  
pp. 100
Author(s):  
Tingting Pan ◽  
Yu Zhang ◽  
Fenzhen Su ◽  
Vincent Lyne ◽  
Fei Cheng ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Land Use ◽  
Land Use Planning ◽  
Novel Mutation ◽  
Spatial Optimization ◽  
Algorithm Optimization ◽  
Genetic Algorithm Optimization ◽  
Multi Objective ◽  
Regional Land ◽  
Near Optimality

Practical efficient regional land-use planning requires planners to balance competing uses, regional policies, spatial compatibilities, and priorities across the social, economic, and ecological domains. Genetic algorithm optimization has progressed complex planning, but challenges remain in developing practical alternatives to random initialization, genetic mutations, and to pragmatically balance competing objectives. To meet these practical needs, we developed a Land use Intensity-restricted Multi-objective Spatial Optimization (LIr-MSO) model with more realistic patch size initialization, novel mutation, elite strategies, and objectives balanced via nominalizations and weightings. We tested the model for Dapeng, China where experiments compared comprehensive fitness (across conversion cost, Gross Domestic Product (GDP), ecosystem services value, compactness, and conflict degree) with three contrast experiments, in which changes were separately made in the initialization and mutation. The comprehensive model gave superior fitness compared to the contrast experiments. Iterations progressed rapidly to near-optimality, but final convergence involved much slower parent–offspring mutations. Tradeoffs between conversion cost and compactness were strongest, and conflict degree improved in part as an emergent property of the spatial social connectedness built into our algorithm. Observations of rapid iteration to near-optimality with our model can facilitate interactive simulations, not possible with current models, involving land-use planners and regional managers.

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