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Accelerating Genetic Algorithms – the Power of Concurrency
This chapter delves into the use of concurrency, with a special focus on multiprocessing, as a means to boost the performance of genetic algorithms. We will explore both built-in Python functionalities and an external library to achieve this enhancement.
The chapter starts by highlighting the potential benefits of applying concurrency to genetic algorithms. We then proceed to put this theory into practice by experimenting with various multiprocessing approaches to a CPU-intensive version of the well-known One-Max problem. This enables us to gauge the extent of performance improvements achievable through these techniques.
By the end of this chapter, you will be able to do the following: ...
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