International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part B2. Istanbul 200- 
Table 2. Experimental results achieved from the map | 
  
  
  
  
  
  
  
  
  
  
Time (min.) MRR (%) Turns C-Time(sec.) 
GA using ‘main-viruses’ 53 68 8 11 
GA using all viruses 50 73 6 70 
| 
where Time: time required for the car to reach its destination. 
MRR: rate of main road length to route length. 
Turn: the number of turn. 
C-Time: Calculation time. 
  
Figure 3. A part of the tested map and Initial population 
6. CONCLUSIONS 
As a high efficient search strategy for global optimization, 
genetic algorithm demonstrates favorable performance on 
solving the combinatorial optimization problems. The best 
route selection problem in network analysis can be solved with 
genetic algorithm through efficient encoding, sclection of 
fitness function and various genetic operations. Crossover is 
identified as the most significant operation to the final solution. 
The experience shows that the designed implementation 
method is effective in terms of computation time and 
complexity. Tests of route selection for a moderate complicated 
network are conducted and their results show the efficiency of 
the algorithm and support our analyses. Further efforts will be 
made on the following items: 
e Enhancing the adaptability of the algorithm 
under dynamic constraints. 
e Expanding the applications of the algorithm into 
broader GIS topics. 
REFERENCES 
Coley, D.A., 1992. An Introduction to Genetic Algorithms for 
Scientists and Engineers, World Scientific. 
Goldberg, D.E., 1989. Genetic Algorithms in Search, 
Optimization and Machine Learning. Addison Welsey 
Publishing Company. 
  
Figure 4. A part of the tested map and examples of routes 
Golden B., 1976. Shortest-Path Algorithms — A comparison, 
Operations Research, Vol.24, No. 9, pp. 1164-1168. 
Harik G., Cantü-Paz E.. Goldberg D. E. and Miller B. L., 1999. The 
Gambler's Ruin Problem, Genetic Algorithms, and the Sizing 
of Populations, Evol. Comput., Vol. 7, No. 3, pp. 2311-253. 
Holland J. H., 1975. Adaptation in Nature and Artificial 
Systems. The University of Michigan Press, Reprinted by MIT 
Press, 1992. 
Hue, X., 1997. Genetic Algorithms for Optimization: 
Bachground and Applications. Edinburgh Parallel Computing 
Centre, Univ. Edinburgh, Scotland, Ver 1.0. 
Korf, R. E., 1990. Real-time Heuristic Search, Artif. Intell., 
Vol. 42, No. 2-3, pp. 189-211. 
Nakahara H., Sagawa T. and Fuke T., 1986. Virus theory of 
evolution. Bulletin of Yamanashi Medical College, Vol.3, pp. 
14-18. 
Ono, I., Kita H. and Kobayashi S., 1999. A Robust Real-Coded 
Genetic Algorithm Using Unimodal Normal Distribution 
Crossover Augmented by Uniform Crossover: Effects of Self- | 
Adaptation of Crossover Probabilities, Proc. GECCO '99, pp. 
496-503. 
Satoh, H., Yamamura M. and Kobayashi S., 1996. Minimal 
Generation Gap Model for GAs Considering Both Exploration 
and Exploitation, Proc. ILZUKA 796, pp. 494-497. 
308 
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