Resolution of Cartographic Layout Problem by Means of Improved Genetic Algorithms
In this paper, a system for spatial layout and its application to cartographic domain is proposed. Genetic algorithms have been used as a way to deal with several kinds of constraints (fuzzy and Boolean), by means of the fitness function.
When a representation other than bit strings is used, it is often necessary to redefine the genetic operators. This led us to consider specific mutation and crossover operators. Specific probabilities are also needed in order to realise the combined goals of maintaining diversity in the population and sustaining the convergence capacity of the resolution process. Mutation process needs probabilistic model and is performed by means of the Gibbs-Boltzman probability. In this way, our system profits by the simulated annealing efficiency. This mixed method permits to examine solutions space without falling into a local minimum. The search process decreases a fitness function. This function describes the sum of costs inherent to different constraints. In order to explore entirely the solutions space, crossover process has been set up using adaptive probability. This probability varies during the resolution process. This variation is defined in order to process all regions of the cartographic map considering that, mutation operator is more dedicated to a given region of the map.
KeywordsGenetic Algorithm Target Point Resolution Process Improve Genetic Algorithm Topological Aspect
Unable to display preview. Download preview PDF.
- 1.Djouadi, Y.: Fuzzy modelling of spatial relationships for automated Placement/Retrieval System. Eurocarto conference, IX. 1 (1994).Google Scholar
- 4.Basoglu, U., Johnson, D.S.: The Use of Artificial Intelligence in the Automated Placement of Cartographic Names. Ninth Int. Sym. on Computer-Assisted Cartography, 225 (1989).Google Scholar
- 5.Cohoon, J.P., Paris, W.D.: Genetic Placement. Proc of the 23rd Design Automation Conference, 422 (1986).Google Scholar
- 6.Shahookar, K., Mazumder, P.: GASP — A Genetic Algorithm for Standard Cell Placement. EDAC Conference, 660 (1990).Google Scholar
- 7.Djouadi, Y.: Cartage - A Cartographic Layout System based on Genetic Algorithms. EGIS Conference, 48 (1994).Google Scholar
- 8.Grefenstette, J.J.: Genetic Algorithms. IEEE Review on Intelligent Systems and their Applications, 5 (1993).Google Scholar
- 10.Srinivas, M., Patnik, L.M.: Adaptive Probabilities of Crossover and Mutation in Genetic Algorithms. IEEE Trans, on SMC, Vol 24, N° 4, 656 (1994).Google Scholar