Real-Coded Genetic Algorithm for Rule-Based Flood Control Reservoir Management

Genetic algorithms (GAs) have been fairly successful in a diverse range of optimization problems, providing an efficient and robust way for guiding a search even in a complex system and in the absence of domain knowledge. In this paper, two types of genetic algorithms, real-coded and binary-coded, are examined for function optimization and applied to the optimization of a flood control reservoir model. The results show that both genetic algorithms are more efficient and robust than the random search method, with the real-coded GA performing better in terms of efficiency and precision than the binary-coded GA.