Genetic Algorithms and Their Applications to Water Resources Systems

Evaluation and prediction of membrane fouling in a submerged membrane bioreactor with simultaneous upward and downward aeration using artificial neural network-genetic algorithm

“…Uniform crossover generalizes this scheme to make every bit position a potential crossover point. In uniform crossover, M a n u s c r i p t 18 one offspring is constructed by choosing every bit with a probability P c from either parent (Rani et al, 2013). The crossover probability equal to 0.4 was chosen in this study.…”

“…The selection process determines which A c c e p t e d M a n u s c r i p t 17 chromosomes are preferred for generating the next population, according to their fitness values in the current population. The key notion in selection is to give a higher priority or preference to better individuals (Rani et al, 2013). There are different selection methods as stochastic uniform, remainder, uniform, shift linear, roulette wheel and tournament (Kılıç et al, 2014).…”

“…The crossover operator is used to create new chromosomes for the next generation by combining randomly two selected chromosomes from the current generation (Rani et al, 2013).…”

The Dawn of Metaheuristic Algorithms

“…In summary, some metaheuristics like the Evolutionary Programming, Genetic Programming and Genetic Algorithm make use of mutation and crossover to achieve the exploration effects. Mutation ensures that new solutions are different from the initial populations (parents) while crossover places a limit on exploitation (Rani, Jain, Srivastava, & Perumal, 2012). These kinds of algorithms, such as the Genetic Algorithm (GA), embark on exploitation through generating new solutions around a promising (superior) solution.…”

African Buffalo Optimization

“…Still some algorithms employ the use of crossover and mutation to achieve the exploration effects. Mutation ensures that new solutions are different from the initial populations (parents) while crossover places a limit on over-exploration (Rani, Jain, Srivastava, & Perumal, 2012). These kinds of algorithms, like the Genetic Algorithm (GA), achieve intensification by generating new solutions around a priomising or a superior solution.…”