Impact of crossover and decision variable coding forms on the performance of genetic algorithm optimization a case study on pump and treat remediation design

Aksoy, Ayşegül
Genetic algorithms (GAs) are robust methods applied especially for complex optimization problems. However, selection of some GA parameters may play a vital role on the efficacy of the method. This study investigates the impacts of crossover type and form of coding on the performance of optimization by genetic algorithms. For this purpose, two pump-and-treat remediation design problems were considered with different levels of complexity determined by the number of decision variables. The efficiencies of GA for these problems were compared for two different crossover operators (two-point and uniform) and decision variable coding forms (binary and gray). First problem seeks the best pump and treat system design by optimizing the location of the pumping wells and pumping rate at active wells while minimizing the system cost. The second problem is simpler such that it aims to minimize the remediation time while achieving the cleanup goals for a fixed remediation policy. Results show that uniform crossover operator outperforms two-point crossover and gray coding is superior to binary coding for the complex problem with higher number of decision variables. On the other hand, when a simpler problem was solved, the efficiency of GA was independent of the crossover and coding types.


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Citation Formats
G. GÜNGÖR DEMİRCİ and A. Aksoy, “Impact of crossover and decision variable coding forms on the performance of genetic algorithm optimization a case study on pump and treat remediation design,” 2008, Accessed: 00, 2020. [Online]. Available: