Parallelizing genetic algorithms
Within a single generation, genetic algorithms can be considered embarrassingly parallelizable – they can be effortlessly divided into multiple independent tasks, with minimal or no dependency or interaction between them. This is because the fitness evaluation and manipulation of individuals in the population are typically independent tasks. Each individual’s fitness is evaluated based on its own characteristics, and genetic operators (crossover and mutation) are applied independently to pairs of individuals. This independence allows for the straightforward parallel execution of these tasks.
Two parallelization methods – multithreading and multiprocessing – come to mind, as we will explore in the following subsections.
Multithreading
Multithreading is a concurrent execution model that allows multiple threads to exist within the same process, sharing the same resources, such as memory space, but running independently...
