Reducing the search space in genetic algorithm : An application in emergence of cities
Conference proceedings article
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Author list: Lavangnananda K., Wongwattanakarn C.
Publisher: Hindawi
Publication year: 2008
Start page: 344
End page: 349
Number of pages: 6
ISBN: 9781424437825
ISSN: 0146-9428
eISSN: 1745-4557
Languages: English-Great Britain (EN-GB)
Abstract
Scientists in the field of spatial economics have proposed different theories on how cities are emerged. These theories were transformed into different algorithms on emergence of cities. Assessing the efficiency of the final emergence from these algorithms is best performed where an ideal or optimal emergence is available for comparison. However, without performing exhaustive search, determination of optimal emergences from an arbitrary setup is almost impossible. This work is an application of Genetic Algorithm in determining an optimal emergence from a given setup. Ten random initial setups were generated based on Power-Law Distribution and Zipf's law. They were used in simulation of a particular emergence algorithm. Genetic Algorithm was then applied to determine optimal emergences from these setups. The very large search space in this problem prevented Genetic Algorithm from finding suitable emergences. The solution is found by using local knowledge of individuals to reduce their search space. The work affirms the benefit of Genetic Algorithm in emergence of cities and introduces a method to reduce search space in this particulararea. ฉ2008 IEEE.
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