Multiprocessing using the Pool class

In Python, the multiprocessing.Pool module provides a convenient mechanism to parallelize operations across multiple processes. With the Pool class, a pool of worker processes is created, allowing tasks to be distributed among them.

The Pool class abstracts away the details of managing individual processes by providing the map and apply methods. Conversely, the DEAP framework makes it very easy to utilize this abstraction. All operations specified in the toolbox module are internally executed via a default map function. Replacing this map with the map from the Pool class means that these operations, including fitness evaluations, are now distributed among the worker processes in the pool.

Let’s illustrate this by incorporating multiprocessing into our previous program. This modification is implemented in the 03_one_max_pool.py Python program, available at https://github.com/PacktPublishing/Hands-On-Genetic-Algorithms-with-Python-Second...