ABSTRACT. Introduction: The genetic algorithm is one of the essential theoretical mathematical models for simulating biological development.It is widely used in many fields such as engineering, ...

When using a genetic algorithm, it is important to carefully set the population size and the mutation and crossover rate. A small population size, or a high mutation rate, might prevent the ...

Many evolutionary algorithms have been developed by the researcher. Genetic algorithms and memetic algorithms are most commonly used by researchers to solve various optimization problems. Genetic and ...

The fundamental constraint of the Genetic Algorithm is that it provides new hyper-parameters to set, such as the fitness function type, population size, crossover rate, and mutation rate. With this ...

The genetic algorithm ran per modality as well, to select features per modality instead of selecting features of all modalities at once. The genetic algorithm ran 10 times per sensor modality (angle, ...