Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Modern heuristic techniques for combinatorial problems
An empirical study of algorithms for point-feature label placement
ACM Transactions on Graphics (TOG)
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Proceedings of the 5th International Conference on Genetic Algorithms
An Optimisation Algorithm for Maximum Independent Set with Applications in Map Labelling
ESA '99 Proceedings of the 7th Annual European Symposium on Algorithms
The parameter-less genetic algorithm in practice
Information Sciences—Informatics and Computer Science: An International Journal
Scalability problems of simple genetic algorithms
Evolutionary Computation
On the Design and Analysis of Competent Selecto-recombinative GAs
Evolutionary Computation
Labeling dense maps for location-based services
W2GIS'04 Proceedings of the 4th international conference on Web and Wireless Geographical Information Systems
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Genetic algorithms (GAs) are powerful combinatorial optimizers that are able to find close-to-optimal solutions for difficult problems by applying the paradigm of adaptation through Darwinian evolution. We describe a framework for GAs capable of solving certain optimization problems encountered in geographical information systems (GISs). The framework is especially suited for geographical problems since it is able to exploit their geometrical structure with a novel operator called the geometrically local optimizer. Three such problems are presented as case studies: map labeling, generalization while preserving structure, and line simplification. Experiments show that the GAs give good results and are flexible as well.