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Incorporating Genetic Operators into Optimizing Highway Alignments  

Kim, Eung-Cheol (인천대학교 토목환경시스템공학과)
Publication Information
Journal of Korean Society of Transportation / v.22, no.2, 2004 , pp. 43-54 More about this Journal
Abstract
This study analyzes characteristics and applicability of genetic algorithms and genetic operators to optimize highway alignments. Genetic algorithms, one of artificial intelligence techniques, are fast and efficient search algorithms for generating, evaluation and finding optimal highway alignment alternatives. The performance of genetic algorithms as an optimal search tool highly depends on genetic operators that are designed as a problem-specific. This study adopts low mutation operators(uniform mutation operator, straight mutation operator, non-uniform mutation operator whole non-uniform mutation operator) to explore whole search spaces, and four crossover operators(simple crossover operator, two-point crossover operator, arithmetic crossover operator, heuristic crossover operator) to exploit food characteristics of the best chromosome in previous generations. A case study and a sensitivity analysis have shown that the eight problem-specific operators developed for optimizing highway alignments enhance the search performance of genetic algorithms, and find good solutions(highway alignment alternatives). It has been also found that a mixed and well-combined use of mutation and crossover operators is very important to balance between pre-matured solutions when employing more crossover operators and more computation time when adopting more mutation operators.
Keywords
유전자 연산자;유전자 알고리즘;도로선형최적화;돌연변이 연산자;교차 연산자;
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