• Corpus ID: 31905068

Towards Memetic Algorithms in GIScience : An Adaptive Multi-Objective Algorithm for Optimized Delineation of Neighborhood Boundaries

  title={Towards Memetic Algorithms in GIScience : An Adaptive Multi-Objective Algorithm for Optimized Delineation of Neighborhood Boundaries},
  author={Grant W. Fraley and Malgorzata A. Jankowska and Piotr L. Jankowski},

Figures from this paper

Spatial optimization of watershed best management practice scenarios based on boundary-adaptive configuration units
The proposed optimization approach provides a new alternative framework for spatial optimization of BMP scenarios, in which other watershed models, intelligent optimization algorithms, and BMP configuration units available for boundary adjustment can be applied to BMP scenario optimization in a boundary-adaptive manner.
Hexagon-Based Adaptive Crystal Growth Voronoi Diagrams Based on Weighted Planes for Service Area Delimitation
The findings indicate that the hexagon-based adaptive crystal growth Voronoi diagrams generate better delineation results compared with the raster-based method considering how commensurate the population in each service area is with the enrollment capacity of the middle school in the service area and how accessible the middle schools are within their service areas.
Spatial Optimization in Geography
This article discusses spatial optimization in geography, focusing on contributions of geographers in explicit geographical contexts. An overview of spatial optimization is given, as well as
Including natural hazard risk analysis in an optimization model for evacuation planning A spatial multi-objective memetic algorithm
Results of the model show the utility of the memetic algorithm to generate distinct and varying evacuation plans that can be further evaluated for emergency evacuation planning and extend work on multi-objective genetic algorithms in spatial optimization.


No Free Lunch and Free Leftovers Theorems for Multiobjective Optimisation Problems
A 'Free Leftovers' theorem for comparative performance of algorithms over permutation functions is provided, in words: over the space of permutation problems, every algorithm has some companion algorithm which it outperforms, according to a certain well-behaved metric, when comparative performance is summed over all problems in the space.
A fast and elitist multiobjective genetic algorithm: NSGA-II
This paper suggests a non-dominated sorting-based MOEA, called NSGA-II (Non-dominated Sorting Genetic Algorithm II), which alleviates all of the above three difficulties, and modify the definition of dominance in order to solve constrained multi-objective problems efficiently.
Local Spatial Autocorrelation Statistics: Distributional Issues and an Application
The statistics Gi(d) and Gi*(d), introduced in Getis and Ord (1992) for the study of local pattern in spatial data, are extended and their properties further explored. In particular, nonbinary
Heuristics in Spatial Analysis: A Genetic Algorithm for Coverage Maximization
Many government agencies and corporations face locational decisions, such as where to locate fire stations, postal facilities, nature reserves, computer centers, bank branches, and so on. To reach
A Unified Conceptual Framework for Geographical Optimization Using Evolutionary Algorithms
A formal, conceptual framework is developed to unify the design and implementation of EAs for many geographical optimization problems by developing a graph representation that defines the spatial structure of a broad range of geographical problems.
Hybrid intelligent path planning for articulated rovers in rough terrain
  • M. Tarokh
  • Computer Science
    Fuzzy Sets Syst.
  • 2008
Using AMOEBA to Create a Spatial Weights Matrix and Identify Spatial Clusters
The creation of a spatial weights matrix by a procedure called AMOEBA, A Multidirectional Optimum Ecotope-Based Algorithm, is dependent on the use of a local spatial autocorrelation statistic. The
Coevolutionary free lunches
This paper presents a general framework covering most optimization scenarios and shows that in self-play there are free lunches: in coevolution some algorithms have better performance than other algorithms, averaged across all possible problems.
No free lunch theorems for optimization
A framework is developed to explore the connection between effective optimization algorithms and the problems they are solving. A number of "no free lunch" (NFL) theorems are presented which