• Corpus ID: 31905068

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

@inproceedings{Fraley2010TowardsMA,
  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},
  year={2010}
}
giscience2010.org
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References

SHOWING 1-9 OF 9 REFERENCES
No Free Lunch and Free Leftovers Theorems for Multiobjective Optimisation Problems
TLDR
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
TLDR
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
TLDR
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
TLDR
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…